diff --git a/tutorials/tutorial1/tutorial.ipynb b/tutorials/tutorial1/tutorial.ipynb
index ccc82ee..349cb02 100644
--- a/tutorials/tutorial1/tutorial.ipynb
+++ b/tutorials/tutorial1/tutorial.ipynb
@@ -63,7 +63,7 @@
     "\n",
     "```python\n",
     "from pina.problem import SpatialProblem\n",
-    "from pina.geometry import CartesianProblem\n",
+    "from pina.domain import CartesianProblem\n",
     "\n",
     "class SimpleODE(SpatialProblem):\n",
     "    \n",
@@ -263,26 +263,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Input points: {'x0': LabelTensor([[0.]]), 'D': LabelTensor([[0.1358],\n",
-      "             [0.9747],\n",
-      "             [0.9358],\n",
-      "             [0.7421],\n",
-      "             [0.5146],\n",
-      "             [0.4536],\n",
-      "             [0.5559],\n",
-      "             [0.3117],\n",
-      "             [0.6820],\n",
-      "             [0.2439],\n",
-      "             [0.6064],\n",
-      "             [0.7745],\n",
-      "             [0.8496],\n",
-      "             [0.4244],\n",
-      "             [0.8744],\n",
-      "             [0.2652],\n",
-      "             [0.1833],\n",
-      "             [0.0147],\n",
-      "             [0.0537],\n",
-      "             [0.3789]])}\n",
+      "Input points: {'x0': LabelTensor([[0.]]), 'D': LabelTensor([[0.3579],\n",
+      "             [0.4598],\n",
+      "             [0.2735],\n",
+      "             [0.5365],\n",
+      "             [0.9781],\n",
+      "             [0.0321],\n",
+      "             [0.0510],\n",
+      "             [0.8479],\n",
+      "             [0.6835],\n",
+      "             [0.5861],\n",
+      "             [0.8708],\n",
+      "             [0.9179],\n",
+      "             [0.1901],\n",
+      "             [0.4485],\n",
+      "             [0.7348],\n",
+      "             [0.6365],\n",
+      "             [0.7517],\n",
+      "             [0.1215],\n",
+      "             [0.3379],\n",
+      "             [0.2152]])}\n",
       "Input points labels: ['x']\n"
      ]
     }
@@ -308,7 +308,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -362,11 +362,21032 @@
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 22.40it/s, v_num=102, val_loss=0.000148, bound_cond_loss=1.44e-6, phys_cond_loss=0.000132, train_loss=0.000134] "
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f87fba27dd664812a8745af6b2adcce2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9218fefceaf42c0bec46893ea92c0a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56d3a29ba694414a947c343ce36c3ecb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef339fc6f6e84fd0a2f126b7f3ffef1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8028551b1ecb4614bce42fe35906770d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b04b17ebea04644bb8861764fae70b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64521c3791534cba8ea09e10fa60099a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d52517dedc154f3d8fcb9285bec9d610",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d43d35e8b8724c5abb9ccf459d4676f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70d2eb3d2fc549f7bf97a08e83e3a62d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdba3ab1f4a24fe8a2aa609d34d31222",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2b344b72a74491ab52c32c53a2cbe59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5137fbb415a74326be3f6a1c27328282",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04c5a12eabd2424fb1ac9194c009deb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40c1c932d7f94f4284e2bdc7a55c685a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f2ef9ec45c141da83b14a001287c22c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5e3d9dc76174707963910cac6f702c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "119e2c4e1c1441f489de5871b72c3947",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7e4dad94575481e82a4a8586b5589c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19fde8aee99d4883b555c07760d81130",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fef2006d491419aad9673291c436af1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80c17d4b605e454db0fcd4233e805f9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6abc2f4bbe3b43bab116c0f8cbbe31d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e398c1204a354f408442ec3c0f6d137c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75791f34626241b39c2cad592daa7359",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5c5b66eb01c4a94b2c14d429733bac6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74464c66e6ba42e1bb80db9c5309620e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6059e43ac4ad494482fa01ff1e8177e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f5e6533814844eaaefdcbec70e72b7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2fcd76f4fd4455dbcc6e7ce99127add",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a4f52386234477786d9d962aafea2f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c002a64b1721481aa69f1e40db038b45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38c463d7fc5f46b7a56279ba8c40bdda",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e436a7c0598b40deaae4dfc2d77e4719",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9eb9050168194861aa4cca39ef73d955",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6665ffacb670482d992764cde1a450e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25563c8f07af4aceaf40886cc1103f09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a6fbe99aed849a7804aadc0f18c3b7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93c57fbc5e1a4a2db954cc0e39531136",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4b85afe70a64f90b1970a6fa79b3944",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b54b4005ae848ccb6d7aa6408ebdc7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c513270160854d73983531607b00a548",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c71d9816c2e4c37a2d3a6562000cc4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c6debc210ef47f8b2a4d00ea1e9eed7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cda94a961474c39ac487be05c8f6b66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87fc643a0ef8407e969b4d648e45870c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb300061b1ed46eb8c16f14e52ab3b26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80f43d102f42444c9277ce80cd5d2401",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46f48a2428b549fc9432f4fa7fef8615",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3ced77f6e464c7abcd2df8227ec1c78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7d2975f2d924e14928a1a242aca7278",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3d5f15b0d6f41a09d9b94104f9ea573",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fdc4abd63aa4181bf7084992cc06592",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "409cfbce8d7f43dd9fb342d6faee1bda",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e27ef4ef3a764940b78908b59909e663",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7ff718324114a17b08f3ebd67c1a54c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b2774893a91425ca5665ad81b61d240",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3829835c4ff4850bad982d3c93b5982",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5781e7f2d0643aca8f8bf00e8cea2cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60f1d75a9e7c45fda1493f412918af5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc95837f0081480ebcd18bd0752a0320",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdbf837181e348068db3f2a7ae3709d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "716895c10f6b463a8ca452097ddd7d05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03581819e86043159021f1942fbb20ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c646c008866349c0b64bd2c9214e6bf9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63389b3a449946dda5b387cbc4dc3390",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d267afbb77e74dd2a01e831789e6119d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81436a8ed5b944c3afea23ec0f95b435",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "daf3a2c915e044d7a6846a81bbf28942",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5f7e89d8d444b8e8306ed848fe90f81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0deac6df469d4888bfe12188259c4153",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc577ffe35b94972b658717efd4f01cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fae08a088d4f4c3aa0f51acab5d5b059",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4143d54b37784147add4226ccdc8d590",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "306dbf3a0a914e0d922153c264491997",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f05327cfee74c94b5f4173e3896bfa2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7a9e14056c0408a809d6d3706ebb3d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41f209e2b5df484ca90bcf77893d473c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82efa05fb8e3405b9fcd832bcaa2a024",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af0e5f196fce42bda2f11668c7c87fa1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ade0c99ef454df1924a4f24f6ca671d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c7d46c4c5dd49179a8aa4bb5ac73f3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35a3159bbd624d1786682a0ee22b85e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4edec7ff54144c4ca4c7b9cec1709a6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "959cf9c724f142d1974b8d200cec9306",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9957aaadf7714bf89aaa86f2c02484fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02455ea43c344cd79844d662c455c0c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2297e50cace49bbaa9af932b2fd7afb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a703c4b94714ffb99732ca9e7f41ffc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "836623a1905f470a97264164f0256da5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c82dbc11cb245b7abf3b811e0d72c1a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "879297e7ed8443c29d1d393501d8efef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03b1e980460e4224ad6813b4d2d47f87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c8f533e88554e43a2b74770efd904cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6dbcfa1c045942f9bf958f743cffd0f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f27ce21a00946b6b33733753301fdd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7ed9a1f072840c0a94f128296f949e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ce7d0dc6aa9451794e10d408f3c140e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e4e3557feed46aeada6df2a68195c8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39fb99af076946b1acc1887a8736522e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a235915ac9bc4bb3aa503ba89413eb93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20596f732194435ebae6d4bfed9312b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40d8f294ffbe4ec59a6a1ed175494faf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fdabc793a2143b998f95f4c27d38c81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7efea59903454f4db45434f2082e995f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49f7794859334adfb20bf5744f5ae826",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d054e77fb54f45acbf3f2049b3d56498",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b69360b347c484886924ddb4963da8c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ce507e2120f499ebdf537f0b6a585f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13ed3196651e4f3295944f632868d2b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a9900d8e04c4106b07bf4c512b24837",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b0f29196d4c4ff4aad413c3965f4c6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5413886547947778e00137d43bd231f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "716ea64d0f4c413f9a28c68e6f1a61d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e2a5807001a43a1abaad6509d143721",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bfa24c87de042b68dfd117ac185cdc9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca3b4f3446c24dfca2fe1165008ec982",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "682ef85ce2e2459a8a838fda50ddffe7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33780d8c2244410dbb153c4d8b6f9a33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0262bc03a1d49409de7ae2812f67eb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f1f02011aa14c74a8486d0a0912c3f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c75ffc029db4bbcb3c9229974395e3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "549d6506855c4ac69094f0fe4e911fa3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "577f0cf5f905440facf1c5d5d28685eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b94313a87e7e498a96a17b82737da6ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "352267426efa4cdbae9f8987eecf2a7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "112c7b1ab0564426a5117a6445b9c101",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f68344ba513d445f96020a33dafdb511",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c0c0edfd19844a2acc9d6cb541947ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72ef996008794ee1a49d9f1c25e3bf65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9053e936981846e6a50327397ee55cba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b495265358fc4a31b5bfa34abb5da5eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d854d37b00743f3971816765c9ceb24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b8af2700dbe4d0f99ea79800768aca1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0a734e7aed442a2a3adb9c255ba199e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00954139fbef4cf490f6f7d5ea6545ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c49ec3cc6180408ab4657a5e7eb7a312",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e92edcfe99094e5eaa5b9c1c6ede2c4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58aac990da8c47578742bb7a64a52bea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f7d8936551d4e62901becd61a89a3c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0dde1d744f8490b958969f1db40b2a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1969378c9b5d481c966f7b00345d1b8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93fa7c26cc1d4025a893a5d61996cac9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "663bbcff312d4890a33aa19c5fb4b495",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc0deb7cf0cb4e77b00225c82b96b169",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c38e48e025a34bdf86449e85e69ade5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2478fab2ca0f47209e69bfcdd5b5d61f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39e6eee998344464831d8bc92c592e8c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7e90bedc49340f6834504715a556e97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "011ea88cbbc04fd59efd938c90f3ea74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e571f69928db46739efdac718863186e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db0131cca4584584b42163bcf7fa4eb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97a9accffaed4dee9d3478982fa3fa40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffec24638f45451480aa7749185a4954",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1901833d62a64377884cb61145850e25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d736b57ff254656a55947466c07d001",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0727cf7e0594473facbc265784b4543a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0af888dfdc57476ba9ff0c9670240271",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61bac96d76994810af402e327b1456e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f97a7cbeeb7408fa91e088d4e211082",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41a0ba7acf5e4053bd96c498fdb6cef1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f4548eaa719467b88500ba1479f698a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a85074d7c1794c72aa47aabfb4f64b7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d81e00d672cc4e878d750bd2fa6ffff4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7cf383db8e04160a1cab000575ac942",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd970d0530a143f188a1832e8db9ae93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d708d4daa4c473ca3df39e5a6abf6d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93b96d1bef694f8abba7bcea747a6135",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7e7336ea82d48179b71692e17995468",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d83f5d13ba804aa49064ee135a8e2d99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acefe220106c4a3085153c882d3d0a81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2e63382ac4640479679e45bdf8f37d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e554035897ec483f8af7ef5ab350793a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b973f2e8096940588505894abd95a81a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df3135f28bf44b54bf036de5361ec601",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1a0f824c88649ed8d262d19f101bbfc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1c1d49b7db6420cb2f5ee68ce54f579",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9292e84924f4129a26b27559f9f5b87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5fd19d6c394b4cdfb8e9864d567bbe0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4cb0018bd4547ce8f4349ac6f0d62d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c85b79dd57a410aab9aa434586e2820",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8900f1f9f89b484e98a28abe71cd3923",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b79ffcc961414582a22a60c1fbb39357",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf08ddb821b04a26aa247b0950a66b44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c05886ed0a74fdebe5d2e02110eff75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97d19795b5ff4d71bf48fd95d41f8c63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a06e1cf02814841b5f235248a7d770c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1c889e411404d2886e9cf22ef4eed98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78f679335d1346a2b951f3831ab7b954",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "faac3ac33f7047e495f1cbf270643e03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e62179fd0fb6467fa37d8a27dcca1344",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00c079d221e74c2c824e3c8e2d1bdffd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0706eec524b94baa8e48b40166d3a6c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ae273a4a9714efaa515e858e5c52dec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52f37cd7265f44a1b4ef8b9221e59856",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5030083513674889907b4bbe481c57f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24d47d0b823e44a085029d06e45933c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dabdaee82efb439f96275ecbb6df10dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7feb1f7132dd42429f71350bd192e395",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80d83d7e57cc4db68fc4355eaf030273",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e02f1d6ac39440887bd271e23a098e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2b80eef0fa342c3b4364e4b63f25c93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be7dc60b680e41cb93022cd2439ef875",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7f882ac0c914bbabfb69bdf9d944c0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffae447d49f741fca35c7aa74cf75977",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05de2525ebcc4d889e4bc5a3836fa6d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fa94a6da7924d349dfc939005418bb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fb1622061c44346a3f0c0e90c279845",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0293b6e895b4845bbfd2ef8df58be22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "945c895113e843268d4bbe2df4446caa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a55474b85a6445dbe7b64960fe93668",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64412ccfb70f4eb7abc046f9be64f561",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d0c8303bc064a418d46766710310322",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f73cbc8d256845c38cf798775b109ee3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "008b319e28154a26bd555cfef0f1da97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35e89220782d4e948ec0256f5e268768",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ebc673af1814c92a16122aa35e39262",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "946d4572333645c8884104017fe2e40e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1992492033294ae89d06a0ad953564c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "223ded913dcb4dfb9a4b8e01567fb906",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3d5fb92927f4bd4a4fc76c5174359f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79aa648f39da4f9d96e57d7ac24ebb80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3dd67cbd7cb1481a94d74024ca15ee5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9232fb29e3f148b39f025262d3e63054",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9fe9faadfda43409400f8a75ed45e30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2789056f8f2b41cead26bced1e0c773d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15d3b7deb3664190803387da48b5267d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d0352209ce14ea0b065dfa1517e57af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2bc713117ee345bc9efa37d513e8d39b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8641854625564422aba6bee7d26d12ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c6a4e55a94c4b008bc26eacd9e15eff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bbb68c6ba8b44dabf429bf1e8ec1bb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c059c5b3c3694b609edaf036327abe25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1a91f68fb8c48fca10f696d32144689",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff52810926f143c7b8ab81b5b18b80b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4e52a99126c465994e73373f007287e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f7efc70daec4b6d9a3917d171560815",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e8eab2c79de4f9ea18f8b1a27ef93af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7786f03d46c4bc19449d76792b9464d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e11a4fc0bf2415f8db70a13c910670a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "875a2ac3278844d58e534741499db615",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "527443daddcd408a97665522506af864",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a95d0d558a19444c8b1f6105e1bbb352",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a7a45596c3e4f19af8478dd49d16251",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "710745a4df354254af68102920c5396c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d48a68da9dd4f68b97e306e31a4a3fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b3d187d847042afa360d4e5a2455ee6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31e421bad0804e9a969c6dca8e21cd25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71850d67162e44c1a862a1a731a770ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42321f4570e14d978fab81530c8396d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bad7b1f7cd5949cebf35e89d8918c47c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "490eddaf65f145ea83128f46c7454a3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0fcc58e755647b5b50072b80f1c2b66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dee45920b36f4099b203fdf7715ea15e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c32b0deadab04c0f940df01461ab4e67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f63b894d12a4ba485e645e2cef423bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91b176bc616f48d581e513f0d53117ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6759095865ee4c4ca44a0ec5b8edc933",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f6204f414db4dd1bac9825f9e7c826b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f5bb01fc1884d69ac4513e139957a29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4cfbf82b567748ab8ab8005c1ba34bc9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c539596e539044f48b7ef7aedf9acc7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "692581e86aca430c915498c946a4f8be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "277f573b88434510a4dbb525c27b7fe0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fa26e2fdcf34ba1a2b2e15015a02298",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef7967926c0c43d3beee499c8fc285d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ee2f2bb46654e7283f1f90ea173eee1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc066bc4c7f04893bba0f936cee6abf6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c559789eda944677ab992af026083eb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b272a4a325434b3cb7465756b3795c4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7eb4e7eb84514cf2af96aca1daf4a5d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4a9b49da7d14753ad41e96e2decff98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76034007ef5d43e0a23e2fbefa794ed6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0749daf1fa05478b949200a419e427bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5167262d87aa4f2282d0c648519e6ce8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "562eac31aac14254bccc5e171056478d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b385c7d57eab44308e5aab2a88777f93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d688d8d601cd4aa4b04d2f0b495f98c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "846ce95702eb48ff8c4035df3b566e4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f690efe752a2425da77d265f626522ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "493da89cc66f4fda813eeab429e25a38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79e2760e379d47d7a8f2e3f3317945c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90f5c22a37554b6b8522cb73693e44bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51aa2c7c314440c4a3b4e082cd27135c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac4729f7701c41c4906a4f46f3ac23b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ad90f30955f4b4186b6c5699224e16c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e6440eb67ca443b2bd26cc5a8dbd76a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c874fe68cf643e981a62b6697938acc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "321dc05e2c71482e9fc670630b66541d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f42ed098e9d8481fbd1b6282a541f316",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "810f3cc85daf4153879410317dfbd4d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b4bc4ca87244e4ebca8f89b52932e24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c8c18a1168448e3ac05d7278f074e80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d3896f8286443e18f7b40811f4bbd9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "629e386140f34dedb8df5b7df10d3329",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9eadab90e28241faa21c80d5cf38b51e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0dc536d3c20045da966c506ceb699011",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2daecee78e21440ab53d84e1cee0c482",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c711beddea0494da6289aa4c70cbc05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73a474cca6b446cfaf743a463765a28f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d2259e9f66c41719be2fd8e40172731",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc3ea55ab4c4499290701c7b75c193ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d13edb249bc24e3486e4f2c8b8612857",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5180b19c0d97429f8dea6e094b45e949",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f171056650f340b98a07f44a94e14883",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3be5629b97e84ca9b52679cc907e6d70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a63b9b3746145f1971ca799746ec6d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1727a830501540bca0d0d7e4993e8de7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac6997200f524845ba21a823c295c40b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4a8c1d884984d8aba14208f17983231",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1a17d6e306349f2a714637c27eead19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83b12e0609e84b4988baf64a931a72c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c780d5f294f46ab96ee7583d3b51d27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eeda4eea1d444119b10b883859233aaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82d816b752b04180be3af32da1f77d20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad28d0410fc54d6fb337bbde613c7609",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2bad7433660c47d5aed4d2ca5c73bac8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c36c455e44aa4c43b5483a75663f0743",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2853d4907e7d4aafa4cb609a1c04d29c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e1d51861fca4814a31777352ba55ed0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da468e29172447e4b0f6f6854c60edba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42d10202df7447c6acee74e6e23703f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bee32ffd4f2c4a59a3ed6d717de3d6ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7602dd4822643e9958c0574fa1e5b7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e92bea739bc84e4c90aa72642a7604c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a1b71bbfcda4a46af9881f61442fb4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3adba7516744c6da276a3790c2ecbcd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1541549cccff446d98d18a71e4ee81c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f902d4ba470c4f7ab61c41d54fa8b804",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0e28f808e534c8dbf5bda3079e0fb0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed4d8f5b2e82430282eafb4ce0079dd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45f029528e7f4f28974adb0c2e079450",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c638a7931ff45c5ac71393e14b22bb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "267a850456e548408d8687b449e7a51f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f174902391843fd86d8d3d76cda18f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "887abfdc4b634a7bb9988c0a5b8fd6e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d21dfb768354e13a3e4c80a28ee3bd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b49b33d404e44bfbbddbaec12ac04213",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b60229f7a164c16afd89f8452a7150a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8aca6830ee3d4af0b77a404e3e8e7bcd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "519172e24e4d44228d787e2b6d45147a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a9bdeea0748440c93cb33dd3045c6e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24ec68382a9945fcb38ba0f2dc81846c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4de6dd5bb4674bcb9ffa3645bdcdab80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5789e070fa4241b99dc15168081fc4e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0da6b89e69e4465aa1d59f13ec9b9220",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d48e7d3329604de6b155f46898371b43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7173459276e6445c85c18d13c94be916",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d76b5aaf53e147e28759b1047df86329",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fefd051ec154460a26f008421039cb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "500db9b939984897b25ef9b0048bd8e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4aed2e7021904056b64a169c0d82e5c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a54e983e58d4175a0d8656cda5cde64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa04cb3e9fca46879d8e76c162031fb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29199f705f47486a90536b23bae4dc1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "927aef2afac945a7808cdd1e34b52dc2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "451744963098443ab35bb487eff28827",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50f742490a514d81a9197ec352948cee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba3348019f9d4815b00d2ed588e7710c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d86d7f8658df49a480e05e6d02296120",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27e7b12c8ff04a95a235a7c7ef0ddcc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c3018a67a1b4d18a203e083de3904a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55f0467243ad4c44bd0c8b4c8456e995",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8cd2efff63f4886801a8f30b71aff4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91c2074410884508b82e2add22275efa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32c2fb9d31c1492f8e07f41a2731bd3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4734057828914cc881cb06099d47dccd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a76996aef454543a59c27fb7dc716cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ff6d6838c5e4b589f5536b88c7d589b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d32155a06db4a6cb3704d9bb1224d5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a97e52d660ca48718f56a0063b745773",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9f3ee215a5048d2ba7421700e676639",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d990f083b100463c8067e2b02b3903d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d963cccf1da545439a27ca187331b2d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53c749dd9d7b49bd9ecbf97b605cc351",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8038f8dc4b0b4af5953151ef53510b32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f94ce6ae8a8433fb17f2f504e8903d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "494db6d256b945d38d9f9eadc9f6681f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "858f95aa811f4015ac1b7b031aee91ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6660d357c8ec4d05991ac780961b8939",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b58c6818d95d49999787826950e13551",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39fe04f5a2ac49d3a3b7dc4b38354634",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f55f1a55c284d5f838df5463b5e6e3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "765005179fe24467be56281d0b9b19ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e40ab78ab3ed4757922f2dd01f66c2c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a529835c5c346dcbbc6019394bce73e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbccb070dba74af8a80a112c8ee804a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e373d652ed8d46858effcdf930794105",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d12f143ecf194766ad0355c7ff6419e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f200b3facd242ff992c002e3509bbd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2e2312f1d794804b3c00fb6b35fc06d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "080667d28b26401287b533e3f75eb2fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f65c807be754f908e569166370610ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e836eec8f1246dda0df66e2676e7ec2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2e97775bcf349219dc1899e7e1c9f95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88162168085749618b7427af9a74c62a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5beb6a5bd79448fb806c03a3e0fd7fa6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9dcf44937b204a8881fdc456f8ca165f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed6b719d7c484c70a9ccbe40a79f1ecf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "430e6a5337354b67a2bbcc2c4ba043c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b73f7a893de4ece8666c78b5c51fa7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ec94064c7004b37945d8f38df032ac8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "101d00df6a894c7cb47c4004b0eb61d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2757b3b00f6643bdb7cb4e539fa19441",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5e9233f9d334444a47e64686e7843fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5438002e3410489cb74b10b22dd033d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c8007099613469aba60deb997a5c1d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea0f33a65a57461fb4e37fe4397d0bce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82278fd0e7d84f38a06d9c592c32bfd1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e6fd74ab668474288ae0a4480b73b3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea372582ad0049399c787cca99ee7771",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd63fa0733504f849c917c240de04b45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af6610c86d584d7e9a1450cb1f3c1099",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "643ad15793d34011b83747d22dbd9fff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "596d0181ed764f98827e1db580baa9c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8d665590ef2461980805de07bdb789f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcdc4a1640624f3da46c7adcab852cd3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ff1ba14e052462a86540c62c4c0b728",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf0a6a2827ee403aa36877fe1896adb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a09f9263017f4098b285bf8a26c672ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea70be7125e94cbaa84ceb4f92aa1382",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50ccdb0f37fd4b52a9e64dccc9d26717",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61463c69ad6d4d08a124979b88f7a837",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ec415001fbe4c5888ef0118498d5c58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3408a646bd3b42bebe5544d06e9fb022",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b33b674b97d84078b057998c79c58894",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edfd96e1d96f490aa84eaaed8ada38c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "153034711aba4e91b466676f81e030c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c54d42e8d7f49959b3eb9c7b5cfdd00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85870f5cd6c64db2b3946f1205292da0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d66e75ba8cb434ebb4278f541b22036",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e766ad5d15ac45e3a8da625d7b257ad7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c495aa34b7e4a8b8c0287235cae8b8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74f6f42cea5a419bb8287f55e9d943e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef984ef2ec664e8fa26e7ba022257c10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7cce32a6be65444cbab26b86ffd9fb21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8212d303696f4da29ddcda4d368845ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c17bf6579877420193359aa6871f2b61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "481de8e8bf24434ca4e5b5f2e9ef5346",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "960100bc02ee4e62a311fbec06bd380f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ade626910f646aea7ef9b3781e8e2e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2636b6c7497546a89618748ca808a0b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2db1016edbf0453da6abbeb6078b5120",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c1caaf5496946feb280ab57c11fb5c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4ae68517e47400ba784faa0cd5d8e0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "44f5b7ed08d34e829d2eefbaa0b0494d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea9eb157b30046b88c88ac1a7307823f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1950b97806c74c479a2542fe1219a509",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61287a6dc3c2478193b71f12896ae13b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1100b4e4ae746548da6938f409caf17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ffe24374a314023b6f8384d43c0d284",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fee842071861411583415428c8684e7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbb6410d6da34f26b1a9ee41013b5468",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d574858d34754a8cb73855516bc23b4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7faae59526b5414e985c15e214074c86",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e0a7aebd8114bd78a761fdbf77a24c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40418eec61ea4e3c84c01e582f0266d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8f49aa4302146f6b1446911c10cf2a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "951bb0fab497458c87c8a17f27abb5c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21de0dffec384694aa7471dab5a5894c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8d264c7e51d41df98ff4b606ad6c59b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58a6a8c3eab942ae82ebd01f7a8b2804",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abd119464f924c399e3580ddb879f6b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fab3b37f65b42538c87623fdbf64589",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37fa6c4e28f242f79f44219cb41056f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3517cf1072d044b69075973d219ded3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b21d6f905e4b4480acd297e7da9c267a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04ec237aed4d4f389603f4476eea63ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "415d22388b324af5b5984d548e9f6146",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c8b68dee866464cbc48df6f8b4ea352",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25dff12f72ab413abbb088ca03539d1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "767f4f4a80874ba1a793f4ad3437ca20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95fc86d01be74c16a9cf144528c9a66b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8bb5a57377748408e652f025665a4e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46875e4d6bb04a67979e78427abedff5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "129a69888f124fafad5aed7a205a2c9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4932f94b219479ab68d3413f229fddf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "059dcb608a2444beac94a360101b6e69",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c36605f0bd846088add43eea3c4d225",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c15b3adb7ab4dcb9579832a8843c34e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b54f1742b1a4a44ad37b3e20c400cde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4881a862be045168af0c6fdad4a3291",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edca1e50cfdc4ce59a1ce8ac79350716",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0955dec27ba4a239f9d38d5138f77ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f31b08a40ca64775aa69fbec940b6916",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9970d34db6e7414fa57f17c175b46dc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5635df9543cf4da88f487ea9c1f286b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2714018dae8452cbe71fd5df928ab28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6eb66cdf52b4a8cb6bdf8405df36666",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2b44b44775f48349d79dbe8aa5fbcc8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e9f2b64ed5e46a59cb0e53f1e4062aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7440046f0ae484095fbdd32e0c88dd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0091330af08948238ee6245b8954aa1a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2fd3eb920d34d5b91e3cdd1b18e56cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ae4eadfad534ce09465da151739b991",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12dd555b7ff24540a8ebc41b806b6c13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5fb0e06cf65c4a7db60314c153858777",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05e3dc46d6164c78adcc8f76a3904a67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9011ce5dc08841538f6c26ecd2a72391",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95dd7ce24d0f42499a55606c6dcca7ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "374b046896ff4521919d2bf66a2c4feb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b617d4a7739480c88d6365805fdcc87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bf75d0b57664db0ba8e461e2a7aa9d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7039bbbd4a6f40b8b48c898c4a47a85f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66e76b863bbb49c08960d0b811f9430f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1820961a2f7341f2b2d13ffa4443548c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "826aa91defd94916a90857866fa0d900",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db49343b36c44ff290a9b912f1264b59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7708b92ec6794a828e808aa98c2349b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fa27afa7b2b4eacb1f9248edbbbe25f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7f56571d2ce43fa9eb09b9223ad32f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da41cf2c808a442a89373d0b9439599a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1518e4cd4778489f8dc70d44b18fd9c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "400ade376af842d6a63d374f180c42a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bafe77fb53b5453da78773eddd395d93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a10c324ff4b4db786cea095a4f636ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36be3dc4180b4c79ab94b90093611789",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9eefdf1555ba4b868d281fe6b0430983",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6faf4ea343f48fd83e7961da29c067e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc5c8016a8354233a0a85e350ce6aa02",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2651c62824d4e79af29ed0df9b751f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe0d720f78d54d7db70664490013718c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8abcad5a2cb84ccea2775630da044af3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60805b25aa2f452c863fa8a01a6cb822",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78c0a601deb54fd1a5be292d9908568c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "033066b3f5f24722bf8ed8b53bb0c1cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58552fb7aac3480a8ac75e4800f6049b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72ce8296d4a9446e8e0592ca3cc0d7df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47603dd1448e44608c237015af5c83f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c547660b427b482b92682245b3550a74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9a8999aceea46d38eb536b3fe46159d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e18bd6a8cbfa46e0a2d92f5ca54e0e7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5f822d990664ba9b733426614879bb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e1fea0f39bf4057a97aa15a0d56ad7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf654748e0d8454991e798d8354a6293",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fbdefe489fb4b9fb06c34a7de37ed6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b3262de69b3489c85e1f46b5af39a03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ed2ff010e69471db721de38c55bd63a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10868a945a7944eb8bbccdcd93dbbd8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "efb70c780fdd4d8bbabed5e0b41d1118",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45037f5a57014bdaabe5a3db5d70dae7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc7a7b5ff7dc493bb50d8a73fde509a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ac2689c0d834497ac87c05d720b3531",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b547ee76928b4df294e787337aaafeec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b6bc925ba8a47dabc3daa7f1b8bba66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6638f5368f634e38999894e25f059797",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "091a28b6b88749ce8c9577551ea81dbe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74adc08e223d4ddd9556a15a79c0662d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1904c2e85fdc4d988274a43d17aff378",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "877fa4954c264ba48f3e9146c6d63e79",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "391069f55c984156a4cc117ae77cbd77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a6f38d06f2e457db2e26d4f19077a35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de018fc57a24488f8930deb7f909b077",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ae0234d2c3648eab398c85b9e9506f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48e5152fd40d462a9f5c2eb41448f508",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d592b3ee337c4072849e9807c334ca45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c96489e110a347ce8402bdb6a4b2eb73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7b465a78a8c4752a70c4bad85a95489",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78a86195fca547bdb249c7463f0216ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55964a5ffde946969011a48a5ceb7153",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b4d1011a04d44ac95bb2ab4d7b0a21e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1e441576be640c69c37a9e53a62803a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c8fd024938b44e2b142b6a51ffcba7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c05bb9abb06e4b6cace4b64bea8177d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb8ad98d33b24e83bf4c545976c8484c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56a8208e2da5496c9379f17ddba6458e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9251af2857441a1a6a07164f767e4d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "148a4c7c0e244549b5fc7f1378482d6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e27102512f84f2595db9f44384e3dcc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3121ff95a3a40d5b9b697bcd201f699",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d541c0bdbbf64b58bf3f90cfaa4786b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec45dfa2d2624456ad19b422e138cad9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8fc92b7e28d433ebbb1294872183121",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e066bdc74d7948eca1766d7e535a42f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae71685a25c5464c936a2d26107fcbb7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4aadde2cbf3449d1b62714fcc43eefe8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e10fbd3aef204c449aa413a548673585",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d52be7b57b0f4f208570037b5a962e1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c82d26bf14264386980ca7bf56beba9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2b42146f1af409885d24fcc1e6fe3c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "883e742a21444b76b52ae81aa3d57517",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "212055786207431b955b315bf89bb399",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72c63b9e755e469595d53cfa5dc18d05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c6e97d66a734a12ad8532f71e6db0fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42872301ab004ae49ae68552cd582cfd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a85fb2edacce4ca49457e4b48bbaad53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ae438028f23418991e7edc06a68928d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "236b3b55cdac4fda80369f5be07703f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ca3313050ab4ad6bd1c01ca0f51e6c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cb7778dfd614569b8de331f8902f7c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1057ed8e9af744c79bd3687ddbfc6f96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6c982efda1b4ff395206b45fdc8bd34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b281a63cceb14759bcf2898aaaa65756",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d90bfb56fb1b4bf3942aac009145792c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d7b52381eda471590604611e184903b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec4c038b8331426fa5c428b104108294",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a1a3f172cfb4cdc9835cade94cca5f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65fdfd5be7a7474faf5d8b93f2ea2413",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "957d9ac5b703462f9ebf392b647b74a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "928503b272c44dba92b7ae15fa713b65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c81e6ff0a5ad4620afafff4865c66176",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ad93319280240b2b56825df07e19505",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11e70af4d4164fe5a77a5863c320ecd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6768a6f05a9d447782b19c78a740f817",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a01cfc43856e4b83bc2b318b5fc3c003",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ecf44a53884452a8fd50eb8e77d2201",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "942fb680818140d395772c16a70cc274",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8ec9e4092614ebfb92b677a74dfef03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "160d919d3d4640e188ee0c9a72cf9525",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29b6234b679d47a2a9b3eb739173b183",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd2b475c7cf54e2bbd3c0de02adbea4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b468e4927514ea098fd315364abb3e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6953cb6aa6aa4e8db13ff6894402b932",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "593cc40c9ddd48f4b9080f559be984c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6f18fef756648a1ad18f7e2a21bf808",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef56489e4e424b1a86bd9a8543e833c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83a44dd4a971460ebb50fea26712cccf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afa090e16d81419f8dcde34626304f93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea3bd703289a44f19329f062ce4b9a48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef4499c10f6440f6a10183b9e46d340c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56fcc3b4c3e345dc9eff061d5e349dee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2611ec277a6340a4a67f4050eb11875b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a08b1fdb58114307acf6468f019c03ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ca17da54070419aadd51f1c6f6c397f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d507ccea533b4175ba96ccfeca7fe2fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a151a3d99874131b93847c835909e48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bc0911843654824a6496db41f9bc5a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3520f3e4c0d45f4b42a2195a4e5df0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75160b61cfa949de885b0db95bd6a9d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "451bda8bbe96411b8bdf0c3abf10c37a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e64cfcb8f59643999dc0ca1854e1b958",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbd6455c3fe54c65b9177ee3b2863e26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e29649afac3049f6b06e9c7add85467c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76d579400c0f489ca244d9dc43f1601f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74dd00149a224bf8a9fdd28bdaa31686",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60eba44a3a3b4125bde6d398112a8329",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca5db3fdb28e40be8c1e7c7324018e48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fba3b7436fb347dfbcbee5de1b7d5b08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f82bffb1297c42a49328c8510a92f888",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d907c2c0a394433b72d625cc83b6f74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17e245f431b1461c9e767ae1ecd2ab7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8941bbd09407437c944122ae76525732",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d9841cad0a34cac9a9b67dff5196648",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ee95b2b56c74aa6b47c7ecd0816f44c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "813edd2d7b9a4e299c5f9535f59e860c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cec9458b0174396a5961198c6129bdc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23bd94fdf5934b758320d254a7603176",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "267ae1d5085f448abca960bc03604771",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f967983fd3f042eb90cdff62147ed5cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad85411239094f70a8c428619890ad3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40ad6f8ebb7a4c0d8bd7499d4b96229f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e3dfd1733934589b9b66947dd9b1a0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "befdc954fa034050a54b4fe2eaccfea8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90625112706a41ad879c2df65a9a3921",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fd7585bbff74afbb7d2fd50cb7c3428",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8292c9e3ccb744f19a3e78805a0c9cb8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "634ea2d1fc3c47479a2f839275d0ce4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f19f62afe31c492695b50606cf7eb364",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42f68cf9a03444ee91db8fedc0f8134c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b3dc4dbcef142bf856611079715f159",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d74b979ff02946c083d647325e4565e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eeb67c296978483398ec2da2ab484061",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "963ba61bd0fc4e778c07f28179c152e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "066843e4dc20468a9205cf9fd8e0ea0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f275a37c379d46a4bb4ed8411fe5b959",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f60003cdff8f424d8f2c9aa4a511fa45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9d737e798654379a809b693a1141023",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60f29ba787c24cbd9ef4ccd922665936",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "766bcafa711d4c6e9363fc94a0b3cdf9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11a337e4d8334593a0a1163de6b6165f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d139685c2294ce7846f3c8991afa5b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3fec96075e74327a48387a5832ddaf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba5fe5583a964dcf9e41ac00076bea0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78b2268c6d884b988c27ce4a132e07b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "640e26ef46564108ac5664cca7606791",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a255853c6438491b9127fa51c2431beb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9446b665f0d4c668b05f52d6edde79f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1df6c1040be348abb1f6127f608d9fab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0311a0bed834130aa6198800037a684",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f5eff6079d84295baa6b491324a7d14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66b91c7d5f334c999ec75448e472c15a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2544b4bda36241e2a53e858b591b455b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bb05098746440a1b3ea5e5dd342be34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30e6279d8f874e919c26d745d84d8113",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8e5b962fcc448a7ade07142226c1db6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81be96018b0e4d3ba8e2df3ef615f3d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a84564534a20492fbd75db98d0878fff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca17f8834ea04f0abbee87f53e38942e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3f78ceba8d340608f4fc17fadcd9664",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27f0770327784e89ae0609ab294bde64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b8bcd77293f4b2096b1265fd8e5463b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5443121cf4514709aca616a464b87f4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc17184da81b4a34a750f05539abc144",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "491c596f7722484cbda75ceee8ea6997",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a685138e66c4cd296609294845a2b6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a6dde12dbec4c7ca7b3646c0ddc7230",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2324c6bafe0c4937b56b9d65a170b0b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6964220b291a491ea6324a20b81486b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b2b6dab80b44238b0490a90427f101f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68324188b54d4182b3c80d530f51c6d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "710abdf922c74d38af02335917b1ab35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5556b4c2e31546e284d19306a7afde7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6529bafe0f649cdaf495bcfcd02ad39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6277e06301347db95e545a98a586e21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8dec216ab1da4688875730dfdb5a7a0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30135289a42b4e5e9d24d736e6b15c82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f7843944ee54e03aa65a0d7c53a9100",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03a0e152561c478fbb6f902f89094cbb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7c14182076e4090a409ab473c9bebd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4aed5ca8b9b4a97934f71e3cdb8ad15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95a17c2031734fa6860ceb8ad22367f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95ed181bc3d64b96a943fa4447fe8e0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e7b96f613544a6289b9ba1890b5fa71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3dbc5ecfeb6c4d27a10a8eb37ed4fba4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae8cb66b031540c8bb53b1d3fc871986",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54918d7c0fa84999af17eb4e02f6e935",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6045173b18034cdab9315b2511042e29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bd0cc04da81436f821cd317e26003fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0eca7e702cbe497293310af59647cb9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0fb918724cc4660bc0b6a4d3d637c99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e55eb3f6b158400899776d5ba9841690",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "122f9a39f5234cff88efcc42462be9f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "992431719d3545cda207833c967b2a27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a806527e17644064bb63559c274713e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b199d03fe7d49e89a81b3323fcc14de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69b0e990bd7c4a1a9ea9d2696e95f658",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c85f5d95b00b46ea9dae8f363eb802d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72e88eeda3574ef6bda9ffc030342cd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce316ccf637b4dc58f68bf5893fcdac0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df05c4a6ce4945f78dd724be6e7852c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9575de938dd24984ad35d87a2cbb0940",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c47d1ef9b6a445f68eed1ec090f0d074",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f87052b085f4baf99d61ec276bda553",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b253b49533f84d2388a84f21136552e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b5323ad0297412a91a8adca0ab9eece",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95669226853f450690b793cbf92153ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed3edbb0db3c4818936d7c2d87509c5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5e930b0b84d4c68bf6de8a63a865e91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d766a44d35fe4a7e89fc5a392f4a4ccd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75636c6b4f6c4053856eb8ae7404e012",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c6e776a2881475891cc553da6918f63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a8ac475c9f4416eadd5eac7b75e0a0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd7effbf0950432cb566b79d4b2da45a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "961ec237d55d4d2b983e425a5ab11975",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5181f18d6a164d54b1e975fb4ebd614e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1417a384ec9a4deea8a5f1b96bd4235f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30751c7bfd644fce98beaa0d49479fa4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "254cb6c882ad435699b8a9b669635b77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31a26c1d854f40cb8750674a61896ed9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c05413f9072456ea74b0d475ebbd143",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e3773b197474bc0b2c72f1c31e060a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "016f3e83177e4e2abb9d80ec254349d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "763651dbee5a4b33a0477b21892cc566",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d273e866d37d4cc3a5002a3d8e8c12de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2599c9570e14f38991c2a1225d6a4a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3d73ba1808846cc9316dc34a7c8dbbb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4dbdad1cb4704fa4ae88515e7fd8a145",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21a0c3cea48e453797028b77b0dfa556",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2312b2e76e444c7ab8d10248e968f6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce16f71ef19a40ba94220e8d0b6b1e1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd50ee6bc36e428c8a2d0ba8cb5d7b8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "854ceec357a84881a5e2c562ed04e201",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "954bd1b90e904bcd9593d9e36106d355",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec58ffb3c3f04dbfbf7b7d28bd155fec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8864a499ca7b4af78c020c16dad0a6aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55e6053e5eb44781b2d199ea32e2389f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f69156990454023b35bedc31954df78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6620f2b71a30429cb3c6169251e318d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7131cc8416944dab872263290381b63f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1620bfe43e94bccb3babb2cb56ab2cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de3c37aa32134cb3abebfc1838779a42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e95543995d844fd8700d2f9d14c9fa2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "846d3f82431b4aeca3c4a2afca77212a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b18dd05cefef43449d74e7a9c8d0a4d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5b10bc16cae471c9699c7c183f4c430",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b1729f5633947cbba1991921bf5e5d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e74f74a72168441bbb692cabcbb8aad4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d64133615a8449839482249fbdbbe813",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "182b8d1f6bdd40bba3703af80822b569",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03e160c46f45428d8b3623c5f63efb5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25e0f0ff2e4c460ba1e50addce185328",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "517e6967c77f4e31bc1bc5ad0a69f5c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a843ad065bf491eaabea99d26dab52f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "799f10ba56fe42b99b6b60575125f1c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1068120da4b9440db1526e5408643d02",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ababd69e4034e2c8332549ad16ec854",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4382c657b5e44fb82fd875af2e1cb5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce7233a1ca7a40f8a5cb1b8b3161b4cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07168812d74041bc9eda1c5c226ea71c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0df382718cb64c0ab395c17090dbe7a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd34da368e594cc1abb20b207a89e0c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f8ce0a08dc74e8a8ee76fd2b9085bc4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "830bec4da67b4136ae346f991900737b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5285e651c4ed47daa62a4731b5cc5fe1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e70303a942a944648e2dcdd0b11b59ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0539c87536054272a113982eea8fd62c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3e92969eaf549cf880534040827d95a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f5b8fa005d14e9c9fec7b26945d8951",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f9fa58a297344e4811f5d69b518bef9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7d452857f6740bcbfae9af6f206a6df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2c03573cea44852b17088b3f288783f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "526074b97a494207802ed870ea5859e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80a084fe3a8a49c38a92103d1d93f4df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85d9a244957647e98f34b76e7b14309f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "210c66a4302240de8ea648d167ae3428",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1525de1ec51d4fa8965893580336d121",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd7ced3cc7ec4ff1b8450cfe079ef522",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8d2d26ee10c47598748172026090c6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "536737529cdc4a3fba5f7dfed04724c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfcefb4fe71f4d0b88858fd2ba06e7b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "686cebbe6f5c44ed9abc05fadf260a13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbdabb33ecc64fa19781abbe8845d314",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80b8bc1f30504bd8a380db3d6f5949ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34a90187dc864ff091055f8c9181a0e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "baaf7778995f4285bf38704253c707af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c68fb58a1b3a43ab95b0cfca7da45c14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cdb9a62e9cc44a6bb0134261557e2243",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73e1928ad3544fb5b4a9ffc39b51c419",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17a3a7344b4e4f2d8092a1b2dd21fe7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4c8cca5d64342cc9c483f37a7620cc5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "904fd7a18a5241168e8eccadd1e72178",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c41a2526c3649f595f3e2fd57de26ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d903eccee78439799aa422f3fa78ffa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f752510689e64a1aa48e0097a53fcd74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "896f7b8772204f54a4c24db28f648562",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca446dbbeffd4b158f64490de1a6a47e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c66fb39e4d4442aabc8214dfb2683b94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9743b99c029f4d519bea7cdc344a3b91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97e6db2b97a142589cf46832d4cfe9c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca85055cb73b46c7a465dd86fa2932ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40960c501799487499946f9c24cce870",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f42d9ad1a064abfbd8211597b72e359",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb4c6f61b9014fffb46d6a0a92289aec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9dbfb4ddb1c4b1594fc0df8308c6006",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1408297a4c784e4f8535932468929c67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e11667b66e0c4e74b0bd4d7060089233",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6382651df87b442eb6b7b09bffff8a3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1cd349cf0ad4f809f3071582eae99e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6067d28bb0b243d89fa44984299e6829",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30721fb12dbe4eb7a3bbcff09beabd5f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2da650577f7d422ca17cef0f09c9338e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4e15d4b3c06418a803f27cf71737f82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9b542dc5a2f46719857c9bbc8bef233",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed70500ef9b84b3990619637c4f484b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26e1d16ec0f745929b021e5d369b553b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60309c2d964542fc9460194c1375b1a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00abf2eb284b48d88ccbb1b3cbce9e85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85bb06e982e64526937b5b36dc6e3cad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "876dc95454fe489daa905ae9bac69ddd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c12dbaa17b242109f42f8a85d26e429",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "984727968415410eb71ac21ba55e0f65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbd2ebc2837e49ff8d00f1a8d6131ee7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "133da57cd60f4bf3b435ed7238e4cd25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c77de0b73764f9b8108ca173cd430a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f115e7771c8545ecaa9af3e9c1874220",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a99802407a74485194ecc5350c1d386a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58ea6a83da8744d7bbde63d8aaad6427",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae83352403bd48c1931f5f10c5c3e481",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eae7decea03a48c2a18eb82479467bfa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1f5e2d4c1cb4db7a8556135ab904adb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "291dd5dfc0fc486a9f98cadaa1ffb8bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6449d12597824ba6904157f9c8bb01c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "181390febb0247efb2f58bf70bd94bab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "413e627254f442bc88aee48029b14617",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b3e81cdf65f404e85b8fbc0794f6c15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b97a35a06cbc4b3a92d7d3e617b68dbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ae5bc772d364806af541cf0523f019f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05ae223754804878b75cf826a84ad405",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34feb2bb444d4fcd9b3f99c8a92b0ede",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8da24832eb9d4b45856644d5d976c05e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59a8659683404aae9af945941d03a960",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ddff365a4f94162a4ca0a0e6ba87094",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a5fc3aa206c47be88285ed5f341a1c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a63437f337d47bdb3d70c2dd5b53d2f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09b0e10a9e9e4e28bcc453dca71813ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29878c2116b842009cd56998874deaf7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2e46153dce243ce82c26e3806f92341",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51c97fc8e2f4416bb51d2efed3bae65b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b2463106e624f8f955eb901910320f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7672788fd0545f4bda836879c9dfdca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f34dc2ad9dd24e82b4f9564582822930",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0f2cc13b4fe43c7abcf83303a26e695",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3264b3b837154cc28c41c8b9c05623c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10329cd6f0394ff4b0c88090b00c841f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37793de0764547db8a53cd6e8a6d8057",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ce31882cb5a487cb7b638699f23679b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdc86b1ea13c4d8d8e472c6dfd743f71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edde73c861d049139394768553baa8dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d927e387a1614fce83e6ace4b6c7ad42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7fcf5b9e2aa4aa29b2fc59f0d833a34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2939e1dde8114b8cab2e599ebbb7fa52",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95bff6be73f24f5eb55fbbbb04d6a551",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b517388bab9844c981eadf90d3a0905d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "900e263159be49719bb25ea789dc9bea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90837bba66ec4ebea345e03eef3fb8e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23db04e2078c463cb9689e6c6d5f31ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a06d5266681349f39d328351f213788e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "197761f937e04c7f9fb90339bc8bdb04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "383530cbd59d47c6a4c35b2d64b1e38e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b31d9a9c2aff4106b27c59eb05e4d537",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0135a18b1fa74069a5d0073adb80bf31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2594df9c73fe4884b5f6542ddcccc246",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9475ffeec9234edaa637f654afffb309",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a97ee94a26044e0bdc792ce476bf4aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c78efa77989c4eb4a4eb7c70b4e5e67c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9a5feb919eb45c9b5093effb49c8450",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c94a6c28e2064e8fb53794633e6a6719",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "990b813959b74c6782ffb78fe27e2eca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fd736e171cc41f7a68b643ccb5fecda",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa2c976bf5224d3db42510e162496c73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ac351e88a9e4c83a4a4341109b62331",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f06a718798954364942021f3a912fa0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9ca02177dda49f98f3778dbbddd0254",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c0a480f04c545299cd298e1a5b7adaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1daf3868d4c343a68749b116cef14b8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22642ccaa6ee4d1bace16de9a528cc81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afe9faf5c2544290b35ea958fec64f7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b7a7065813c4491a03f6ac4cb1cb09f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9501fdfe92ae4905826dea36c15eb916",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "061377e449014a9eb205e2edc6ccfcca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4d7443acebc4b3babfc88a015fd80b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5361b8b24f6b455ab40960012942788a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d5e289d398e4d9bbc50684e53249719",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa19506892d04b72ab0b1192ef77ce8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa1d3074b82d4b0cb361339a791aa737",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec85c16067a9427bb4f579e200055162",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0640b8905d94419597986cdda81df335",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b02dc065b4ab4f208a3345766e9f565b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "657550764b8a4fc29648b9cd9e2fe145",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b33d76f2acb44d72959cabbc6a20002a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "066460572e7541a6915909f6f9fb4584",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce8fcc79e6574b55b5240490ff4e883f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1618b3b7220443f99bdc4e79bd04ee7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "797728ce6dd74f18a66a344d0c29cf35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3af815a7ba1341e39c769e66a256f73d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f402e949a1454193bec685347c96c109",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7422a06480aa4cd8bdbbfcde86601ddd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e987373e3e9648a3b44ff1df19992f18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c7988246a0340768564db7f4606ab1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a393a100816423487787184619f9080",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a0ba36907ab4330b92925af6f246dee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16f7fa3df1944728934c8751d2f15723",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b5f5c3962034bcd898c14354cf4596f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3c0fa51325a4e7b8e93bf1ef4d7c701",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24278aa0bc3c41dc9aa8beee5cd4fcc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a975fc96499453492589606beaed1a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96c7378c17064d52b7fca9b2cc0d4864",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c45ea18552664c13996a9dd464958653",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd9d94030cec413087a67de581be0880",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d846db37b3aa4b909ba9ea837631fd4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70964f8d2a344c9d9b6f008ecbf19290",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0c9b7d424e04b66862804f2a4060b09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36e821f60f0745a7a2da0fb6103e065f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4259aa74d5c44f4a8608d6ac9e337c32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "741c5bfc01504a7bb1a4b9237682348f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "681af72327ba4989acde55a9e019d89b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea3db1f3d95d4a40b57665c434a937e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a207576b6f634776a3a95d8195c3bff7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53430d9e0b5846aaa7b8d05169332cf4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2048a76cf3bc42fa8382cc8888a41546",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c1e6050e8c04d6f87594f9183480b7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae902e36423b4503a7e4f76d29968d6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e77252b1a29a4540a5a95dcaf91faecb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7ab4fad2f4843b58d707542e6b1b089",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a09f5f52cd3405f941dc3031e593d14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d70d374c3e14756b8d2e7b5b711772d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31ce377128d748d994ed2921fd0aeb20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81966cde7ba84e0bbb0fd7f5f20f3bc4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a99689f5695241f59e24d87f22048d3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2273092be56d4804ad9dea22ee75ae5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc0832543c33461e832190b89345e048",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9858afb62de491297575e9fb8a1003f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "268ad73b0bdc4100af0214d820d86f77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bfc1542ef8b43c28c8b00463ea8fd27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8434cb20350d43cab7510a333c33501b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb3bffd0b7464457ad2338ef06203c09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3d2572ca4eb40b28b523130446b2b2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "864a8aa2cf7b4dbab5848ce732e7613d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac1d13ff76244cebbba1654993048767",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ffd213b4ff644f28e99d06bfd862b65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c34e51fb6f143e591c19b2af3aa7404",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c8ded6ad8ec4705b4130b61c1c49e1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "979a246d92c74667a7d77c65229c4afa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c39bb9637985461ab9a4700142dab5b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e49d6a958e6042238db1a814c7c6ccbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ffc0a905ec944fd88068f78ae36363f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78c9be880e274f70a092bdd1eb37f3ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea737046cfab41a286f22fad417dcc99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b599406c0cd4dccaa7ff14c4b0268db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8d416cd966446788c6bf64cf12511d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8f7904c86a74ac28e0b254856921fa7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb6702ea264943e69b03de2f614e571b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e42356ea28b14757b25f4ad40eadbdfa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36331d60c4724a1c996d87e29c612031",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61f26b163f7e4fb9bd67f91a3da07c8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ad6c903d2c5420abfedfba328791a1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "016d5a8d7ccc44499c579e587b3b339c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63774f6ba99541cfab110a41d8f92305",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd2ba98324da4d59a12965135ce9ac66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d917bd12bec4d3b93313bbf205a743e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1dd91c9ae48344e99a60af067fc64b19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2987f97925cb42b6baaac9449e177097",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "536c42dc62e240049da69afa498277fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ebb4c82554a4f258a057b8d0bd64da3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d4d5842bfa84689ab83b337a8164ec2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6604de2d9fa54dffa69d8ae19afe399e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80a630ae032a4463bbced2c3f1030abb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8367778305974c18a33cf2c777e3dfdc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58be8173dc4d4f1796527e3b395f9e21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfba00c143e0481996e1de210d1d4b91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c72ce485b6b474b9e55be7e8186042c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1dfa85786f974db6b9bf825b952b3e2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bab8d327fe543d385528070b7b783d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39223689e82e427b98b613ab21a13dd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82b73066d8a54780a7453b8711a303aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fb6f5edfb244151a278e7862380f575",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f9a0acec7de410b80c0ce87648fcc76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "550e05bce4074d9a8c1119c34591d879",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "414b0bdb09d3447e9f21ee05b8b99809",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7055703059d24da4aa4141f37a7b4f3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "426f065b63a943a4a6afbf5fdb8db187",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6cbe938d4e140a3990a0cfcc4b036d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "918283b6cfa0448fbed12220d974c032",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21ff3113e5294759bebc074d3480d082",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01e7510e99d143f5ae370a6840e7053f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d15d55ba488b444eb264f5138092b332",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78dd598152d948aba83bcde1a7f10072",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68d2a1e6edd14677a5da96924183b23e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9888c8e97774a89b0874d470534b1a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "127c6b4170b049bd88a1ca9d94347c63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "436c161fe9314f65869ace12f280cc17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b82afdbc431b4e96b2ecb661b70bf22f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc7475774c5a4574a81948913117f6d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a25297071be1498da68df6557dd42ae1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a29682c1c3a441f8d5da608efdd9039",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35d65dbde7a1483b9fd54187d2f6fc62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "554da3574729435e860519cf30638f8c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "833525f2a6574a1da44c683206fd431c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c25ffa43a40b473da6506abbcc97f454",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8aea346752c44ab8de6af280f0fe46b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67dc808d5f9b461c8cdfea07323cb3d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bedeef5d47a640c9ada09ee92d76646f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54956fd7504142eaaa2eb64ab64e5245",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5aeffe54fe0493f9637aa604a6d6e10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c60223b13744d9285c572a2e8da333c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "350690ba96d741929c6ecc494a898960",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "752cb9c14ac1442dacab5219830b3374",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7cbe8de11dc24c0a8580006b927842c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a9b88c5e3bc40a6931043fa5297d952",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54747aa51df540f0a1bfe6f313c1d6c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b147b044ec3c494ba65b678b529a1b22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0600896c9e6b409bae554e4b75d399b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5b46e4af7ce4c3094ad45957a98ac21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ec5c2497ebd464ab2d2b5a149e9e778",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db8d3ba98163435cb05930764538e0c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "877c7cf2a5e34af2b97af1de1d636a15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d6a0dbd22194afda7a7a5403deba69b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0a742e4616f4d07a20851c138087874",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee42e54d7dd246f5b0c14905e6c3721b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afd57ddd72db412c9badc744d1be32a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a463a023f7a4d2d86a3c7147cc2652c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b56112e9d932408ea99be643761be838",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d06ac8208d0949d6a8cd9ad3f76106b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c18248a6b2d74abc999a8ee650deb13b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1481d1e697a4fafb75fbf10a02a9428",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4562b5970f9044b7bcd7671fbab4011f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b78057eb3c584de49265411c3e3f9b2d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e594ec50f694650b694d0e47881852a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b33f5a219e944e0ca45b79682b3bc3b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12e0ffc4dfc24d8c93792641ae4b35a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "334425a6a8be4698babea637e67d4ebc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cceffae9cec84504b002aa4dcda69285",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3cdfd472b184f569e72536e3546c43c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70e967a5edec44abb3ebc14512d34b59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13fe9ef5aa464df48b86308ab5556400",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "788f6a4f7a3f4f458cf7c8e254203a11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41a7e1dd2ba2468c981d4ba7f64148bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "341f3b2c4ad74925bf14e94c068251b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "869dd130a5a0437a8d6ab6ef993b494e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bc5c0a37df74709a573f15e463b9120",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5de38af3e474406a947833e3751c017",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0528e9d72244b089009ed64c8dcbfa2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ee8225f379e45a182ab2288e4eaa9ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8beb529e75664669a1d38cbb7dcdfda4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d8d6d3a3882462bb972f4f268090793",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "989bb29477f2421b96a8ffee1a000488",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f4d2b19858546bfaa7712c712f254e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cdc00bb6b6fa4d968dbc0303d2428cbf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97dcf76b813a49d89bfc89d2bf628926",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e9be7e780de4745b3d522144f820db0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6fc832dc34f48b3a2c5f214060d0246",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef1437c4edb145d5b8330d38e2cedc01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cf3060efe4d4cdd96ffb190ccdda81e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ec43768d94b453098da03e5c01e4f59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f34c17abeea0462492de7d5ac42f1f11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "057656cd9eaf47e6986bd54c3958cfba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "784dd755e6004b48a58d93812c253ef7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7eb8910ade6d4c5f8be07389e58292c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef904af89c5e49109535b741543f8d10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce0bd71f78a44d098a393ebe5c6e1c09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c3b28672b3b4b089b0736509d4b0fa6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f7483ee2f234eddab3125843612a406",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "498b60261b2a4b009b907b24c909f828",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b5ab9c80c334a439381d12e71b80047",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1978d3b0ebc45c794f42361cb231067",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a170ced4e2f4258bfa3fdf30c936709",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "958cf98c998b4d1ca70fa5321a33f57c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76052b625dc446bdb60f379ba6a6f2d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d733a4f22744add8db1e89d0914dcb8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59e4a1a1d7e449119bb9675fe0789076",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9fac3d05f734126924fb00aab7576cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e12f9a68945d446ba46ba532f9873bc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a132d48734b74684bc9a556e71a7b38b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fe6d04d004f461cb30f359f681a14c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab5d43e3d7154ce5aa9bfc408326c658",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d7d9f08f8db40529f32d36ee9a37086",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7501ba28c0c34e46a79123ac0023223b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b95c486369a4b6a8a59f4ac45120739",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e72103cb65f4ab59bb370df5afb7014",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b31f31d27144a5eb2a593548e14cba5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06ee73b3000e4e6ca050ffdc03ae7aad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "237305408f93473291f5e932c91393a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b250303380a476aa3590fdde69f60a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f857f641891404db57d1aded91bd53f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e469cc406e24f47bc77e409a03b75e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fce536d9d1849e191adca0128780650",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8db9dc80e413452e90657e6cb58e0399",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f36535f6b8dd41508ec262a6681955c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2636409358c4b5ba9ce85e7abb8dfc4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c921c5dc91c4e529cc24fd0a8fd09e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eac3293818a64eeaaaa90bb0c7948b61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb3900a79f0d49f196d1af7c4a674211",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ca09fa3a3574cbf8e9926bc2eccfd9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "208c1e84c6014f38988f86b19a4e9fb8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b94dba2426d41aebae83f6e78d1662c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "695cc1be54e84665ae2f1e90a8d813c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43411706aa994502b6a2e48127eb2fd0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "988628e81ade43a7ab17891d6d637592",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd2f410b65ba4a03a89576f6733e26ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d57ff0d0d81447019065af8c719f6a94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01776f586ca44e4ba82c6241a3bec771",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b2f80900e77484bb9496f9bb85e9bed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e36c698df33e4156b91ccbc641ad2d4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b98bf8301f5845a9ad6cd8f1249a8295",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "811a0c0f30b64e558ad43d65cca3385a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23a72490b3c84da893522ee58e2772b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40cbaa10602946d281e5244e23d91ec5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cd555a55b3d45bd8d5b20aa97a5fa1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1ad73a683f44e25b91cff38c51447e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbdae3af318e407fabd8c6a60689b7d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed5b186e8f4f4f35b488c4d9c7f012ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76c4276c748d4592b832cc9d9fe45bf4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29206e45677b4a6a9dfaa57c282a3481",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d811adbeb6ab4fd2bcd1c5e90098477c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ef5acd8b9a2436a9c5f2a9cb2d3f6ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f0b58087abc4920aa5571a87e6c261a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f2f7139b2c44abb8e4e840972a9fae4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55d9e2f3c41f474a842dfba2eb01b020",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7a07b6baa254981aa4fb448d09a8b6c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aad4a2577a5245949f948c0bc321c3aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8dd24683ad034c49ab1f62b1586d0ab5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7cae670a9c4f44aa850b107944f32137",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6fa2fdc427d44578ffed309264cf1f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ecaf9c245c748bd8673f6e5ae3b1e0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "152c3cd7ea7b4f7da0487e4d4e791f1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "657ec7b3a3a44754b1c044e7c62a6ea0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce7e670ef937451a9dd75a98de5cebdc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6065e2883cbc4e588899ef8959e219a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9eb6231c385c4403b5727005483692ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56215df8bcc74b0d8ede532abb54d367",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97813a2ffd8840efb959a2806c560ed5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab527d7cb9a6484e9143bc0c1fb78f4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd454974c47d48c8aaceb975dc1ebb92",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e74ca5317ee0450d84315f69f1190cd0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fe4221fc6eb4530b2ce3c8229d0610c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2dab986743e74625b9f5770805480adf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50fd0c38dcc54d2e9cb1d35fb7ac6f90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "caca834dc9cd49e789f86d935d9119f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b68d76e085074a9dbf730aaa04a6c7a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebca7034514e427fb35026e5897e89f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9bacd38e06b4a3c9f46b9168f189683",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05b66076d65e4d22b0c1d86f5ecbf58f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c805e2eaf74b4d8aad2803ee4199a7ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab8794182097412a9f61ff0c9da0ca3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bbe9e45edf8442f9ca33544e7427495",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe175e1c168d4fecad107d9dda14c282",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15bfe99cdc5d414bba09cb3789bd04a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d48954b26bc46888af58e1894b6262f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3fa5f1e3ab34866a66ea597896f8417",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a79a8d1ef0c4e9e8a5456a2e6db4a1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d795daa514945069e281ca255a8561f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f50f7e720df457fa70489d90a4400ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a1831c79503403d80a22eadb1035bb7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8035fbbbc0f140119679af8a0d2d6c9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2f351763db440e2a3ebed53a3f03917",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9be2a680d1ca4d5c84b7b484da42c689",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4edf2d8846a415895f17992ab0a896b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89c5f5fadc384625899de9aebb815fc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4147758a5f9145ec98acf608f7ab2a2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fca0d4a9ee340a784022f5cf56a8dd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "557255937be04812948ad212bab555c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd9380b67da74c188395345abb791875",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2228c3855e9b4dd195648852b772c966",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35cc3424d51545b5a9582f47f8afeec1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "284aac93d1824648aed73e965b7acaef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79b73e48ef184ea493ba870129a05fa7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfcf5533c449434194a338c5f4d9aace",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "efb87f2bf22a48c090893a9c9b859a76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ceda32b0766349beb8f2f6d74eb3037e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e78836bd49d6445f95d9f5c1da5522f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a7332b10e2b470eb7885e93e754ce9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "557fe847da41401f8a590a7fbf565eff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29fa8edbf8794c5585123d8329c9d3a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a43e09000224ba3ac96d906c4e5b225",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1935abc4d064d1f8c355854d1938a7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a94ee1fc71f420f97d8c799e5eb4126",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba56663e7e1e4e3ea90343c0e4bb2f8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3e43e013aef4e22866d018b991f8d63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1634c553637e438a881027b045cc97c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4a540d2d8ec4d08a631530164d16f3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "040d4a4bd9c842f394c2c3faed73415a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "010f46d962a544df95aa827d70278890",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d03993a24368476aa205c16501ba62a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a14fe09174f446da2f1fdbaab02c65d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73a3889299f549a49ca1705795b0bbc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5776048b5d23484aa592ea132ba24b21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e6f1bd5033843a6b4da93b62175ec63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ed1cd570f054e66bddadceb9132902b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99e760fe46d74e13b39b4d7913507661",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d47e3d7aa164ad68f298ee8d5c43ef1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "289c6f8359aa48248bcbd14b0a2d294e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fe2875dce344ce08ca023d4078cf1d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e12f7907c922414381b6783fa1abb0f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12c95035469d4280a02dcab43ebb0949",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d199cd56d7ad48cf9cb467aa5d7ddd14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbf9915342b54a088779397ab21cb689",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a650953c484d459ba1578d319be8896a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3674e7ab7e6546688ebfb4e668446aaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a05c1fe8bd254095b46e5377ac46a639",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b566bbab82de468899ee7044bcec8a70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2029bbaff62451fa5144f747a07dcbf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "747107508e3244248a2948f6a14371be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1432b2f1a434eca84a61f290de47673",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "765abced3dbd47a9816c6bb04e911b2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4086f1120d044b0fa0c5dd786b803811",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "777f4e7024d04e91b4e9e7f8641e8680",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d04cc1fe371c44e49a50c753ab952dd1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ddd84e9fe93d4240a5739beabaa92087",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e22244b8d864e1c98e4d0c6351c27ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eef039a1cbe2424f9f422fcf539b10cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85007cf568cc4154ad47b234e6b17b85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bb2e0240f9644a39ef60461c6e15f19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "915adbec994b41e5aaebd815e513187d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7809c00dbfff4a6fa7d182921d21e8b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26e9feeef8f842e8a23900e397663ad2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1bb893913f74070ad90ea5287f0edaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "998e0c9854ad440fa660aa4140fef1fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a569e64f17174a768723923f46ecbc59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d49529e54364c71b57231ddaa234ab7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "894bf70e9dbf45bab80169190073f125",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0325081088ef4f0eb5354be669593ea2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e42520004d6d478aac101b73b314a019",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd4a66db2bcc4fb1b58a5ab63d9241ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc9fcd2ab30942b88d5311946cca150f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b3890a154fd46e5a0ca374d0b875a90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58956bdeeb244dda90afa3b015228091",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "819c2e928356486692b2e8ebc31ad906",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4249243f9216473880bc96858f3c2ad1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "791f4e65c6b64ca3b2e32d491cbbc4b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be5bffd00832425ba0379a419b529520",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed9ffb2c63bd48a1adf4445668473334",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bd69c64ffae464b9a41f5c1ade196ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e63b2c06d93548bd833111fba3a0b2b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ac63205a0bc436dbb7b77603fe80bcd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a2eedf1147d4285a5ba98729cf47cf6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7588e0792cf24929acfcc54a67dee95c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "078e5943c04c44edb9ced98a2ad230be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ea996a8d9bd4811854a5d547d48d37c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8386ce4f82864e409d4fb8df27a83751",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c73799c3526a4d65a23186801620e4c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "917dd79d7e064348b3109f1e0f2bc86d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdec53c448874faf8a28f434c7090264",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96b66f0d40fb4485a9257824f5cf302e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47de1e38b0994100be364d80ddbe9610",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36954e6610fd475592ceca82e4c6baaa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee8d56f003cf4628b240188a33b2d268",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45bdf6b280744c37b887b371731abfa5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bd093e5989642f99b0a00366787829e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dadde34ea9d64c73a1519038e10c40d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bceb3a8030714f2890b641cf900bfd43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb783d27f9424404ac9085a84d363f35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7a738b0979a47bf9852a5b69172d1dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40c867cbe12742299e08bc60e80e216d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2866a95597c4d1b966b764209a1670a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68dee321017a4b1ea640b16ee4cac317",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0d3a9559c6b4a4eace1c24c46fc5a47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcd876dbbc964b8aaa5a80b654d5c02d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fed401b8419458e9c94200d056ea597",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d455f13ab8d435fbb94b968c11860e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64562f7bdf804ffbb17b7910c1876b6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36a5c604133b4c5da3eb2a127cf13b54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "821a319646b1415582dbafe50370f1ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99ed015ac51847bcb35f60f209a64638",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3e216718eed43d899147e0fc0869be1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58396dc0c70c45d7aaeeeccd95293338",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fea2634aaa04920bd1b15f2d1f2a688",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0c8d134b45e45449ef3f10fe1dc49da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf1af459cd1a4450bb16b3612906cb54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3697bd499454d17aedb70afb1430935",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f256f0052339466094589e2ca6989fd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b089ba496eab40579b0fba447aed67ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcacd996c2844b448e214697531b75af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50b0227c13e94fb6a981af1190369977",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b168b65538242b9a12f91f9a0da5249",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22fce066286640dcbfde723586cba743",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24fb01d89cde49de8dc15daadcf8e946",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c92a44753fe8433aaa0af8602f3e7f62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "762738f8aa2f4d92a5d23ddbe5a72b3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a871e20f61f041918cdfa625f9880752",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1de7629c7faf4340ae9185298661b9b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88df23e6733644318effa8d51cf59e39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3b7844934e34d7e9eec586eca5e5bec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c75d722835ce4923bb52dc6fa05bd04e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c822b1132dcc4b908e4d7793035c4bdb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fba42bd30b264b8facabe7664a11adb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "666889da37a141a69253a32e26a9733c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f3e83d9bfc346cc8bb078897296154e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80b8923499874c3d8d78306f675ab675",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0c031759671421597d379aaaa54dd81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ee8949c6e374c14bcb285d0286f94c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab21eda2e8f649b7ad40755a75ed840b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a05ea8e703a4312be106b3cdec702b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7af95a960de54a329b64334e953499e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bedb3e014495428685a026a62948b66e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5617315a55e464aa4ee3b6799ecaac4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd6c2b09fb3e4a94b2eeb6c8dd3cdc73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ccfeed8c8ea64ccabef87df9b23ea6e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "08db70afd5974e5f8263ee0104b5e625",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c700946375be49c08c95cf60f4a9eb88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29957b916c7f428d965061806836a95d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50a20d7a4eee4c19bbbd6e91222f77e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd2569eed408495db439e40f78e7dbf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "181595202ef540cf8d22954f172ce79a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e0ad78727d942b992346ce5dea88d36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e1e27783df04b62bcc961a17126f481",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e797203fd104b02a942c04d6fa88072",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f17f51649d4a4fb89239d92668304a32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b8d23b8b1c443a0a1ff46adeb6df0ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3cc554a87e44c97b7a970675f265ac2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1daf5014bce242d78be23c257e4fce49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d4670b53cfc45bf8ef5b49015de8682",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd8084ec0d4042018ab137a60c3e24a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c43b466b36994bf686e5442ee24f1365",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71733b0649e04b8aa2bd9b8fe22b923f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81df6afe8bd041439872e33068020eba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a647837d423b477cb5ae8f44c0cb87a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "566b68dd26384fd881b537fbd5175781",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b616cd3e33574e1eaa4e361128838431",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d94c819411941938e51a319c06faa6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9f63816681241ca928c5650a43c7394",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "095ae0aefb4f4fd0bd5b61be7259cce8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aba546d8081d4e5b90f712059b46f206",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8417719c3b534f369c5375858eb071ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a815bac2ce047a3993f1074ce596292",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "021db32bf2364b66ad31be17065ec0c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54bc068147b249a6a25be467ceeb9d5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01f3b360857f491d8930b75c295b179f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b1f9d12bad94d2c9493f51af7ca7de2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96abad2fd9754053939b81d981cc119e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee20991c435542dbadbee34da1bd571c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b74e380e766412ca2ae5e3943d698a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b105eadc80b640c6984d2d87703bc0ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "975e823d9ecc4594b4d915d936aa1925",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4166a9286a1041dd860111fb3e5b814b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3a2ff932bd943d7827f8d847d4ae4f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36371cf7498f4d34b74082e2dd556326",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86b7daa1e6e64aac8c551143387ff13e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9c4768211c34c7ebdb1cb8c360d74a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8af15ffebd5549a4b0c29a88192dae96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0af853fac7f4ddb954956c9d0847123",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b86477c0f048460bb559c4aa0e79ce02",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f94f3582a0e2437ebfe7f6e5dfb5695e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "205183d65c384745ad521f099adb1f1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "44d6c2ac85ca4f46a7216708acb16cf1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59cd81ca92cf4bcc970660249a7501cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31bca03500614873b3ce51652ec81197",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e0ab61d4ef14d92848592f2a300ff0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af39a2a137ad47d8b43f46f4a2af21fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae75134dd21449bf8fd1d7cb6ff2cec9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42472ef7afbd417c9fae04bd3fabd505",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5af7f2efc3f34e2397685ef30b9d64a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5832f5cd594f4fe4beb63f916f8701a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "683108791d584e29bdbf56cd5b185682",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38e49862231e4d74b3eb225c8f0dd86b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11fa5bf9fe714996947e2a5059c27751",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81e1d19eca0349169664ccb110056f53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdf9a3a880d740d3b309244b61678704",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63ac794cfa0e46238cad8ca963bf930d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5578036899c46a299dfb926add956d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed78c72b0227459eac21f0b9f9fc0195",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "111da192a9ff4a348ce6a7e98489d585",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db35ca77d6a346c58c7410feb6f78e53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b8a7fbee0dd43f999cb082c2c941fa0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5bc01e4683874e21986a66daebd6de15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0750cf7ffe2c44748104719605139957",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cb7bc3c1eca4fd58fac6c6b9f9bbc69",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab7a7060f8ce422fa1c0951080fa91e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81ddabc481ed49ac8bffe9dd70067fcb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec759eda5291449c9a57895412a83aaa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4175a9f8cfb406abaaecc3ef32b46ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad1cf0135b3141d8b01e14bea03039e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db8485aede5d4e4a8d62346492c6fb45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fea9df6ce6ca4d519223dbf8bc47a649",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cb8ca6c5b9240699b5e79e8362ed38b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32db588b01c545b6a02bbb9a2d619523",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93462447d383459894bc9b345e5cac9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94fe24d3b1b14f2a84b0a8f04f63281d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa90ca714ae14c3cbf892cb1c00f0170",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e86df09af11413eb49a7f7e6ae0803c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd271e808ecc4d87abd1820264acefa1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa46b7cbe4d54f01b7985b10c2265073",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4391e69cabde40b584ffa18e52d1ca06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd9bf107b0664d81a38e425b16c12197",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "432717a999ed48cf82a9d66ec34db418",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d5aa0758ce8423ab905c5ab4fbce0ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "684c1cbe22d34235934b9b1d76cab666",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6877fa6fed96431082a1445704883c10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e02424e3167d43f286211cea4bb8b60e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "754d0d620e884b2181a899af0a549e68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6627a4019b1c42fbb3f1fc56f890eda3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8479471d2322431e850283c51c6b3d27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d07023fe3fe8435491c3cc28976f1840",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a989d73825884c5dbcd74fd3ac87d91c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dec31cc736ea48d0b49d195f601d31f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20fbc4b3af4c48c8853f5cafede6c46b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "926221010b074da0a63c3f4f58190d29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d7aa30aaa254b7b91368d45a212ac41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7e4fac7e7bc44f4b438f92c0c457f96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8f9ea27befb4ae7820eabff85631957",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f7b5d44cc4744fdbfb85a2de118b5e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "857a7d2d31b74ae69386e19103478342",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24427ab7f62c4b02bda6ed5b7e3c4422",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "680900236dbf43ce9357c5cf1b5cc8e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3061eabd59de45a48c423952cce78dc9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ad2c2fce747468687b22cfdf08bd822",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14af4158fd93491596fd8862850bf058",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ea11c4668d34810b15edd6781706479",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea246be436d148b4b3a1d30fc24a4007",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6a7b4f58f0847a6aa56458beacccab4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2cd54bc045646f686099b8a64a0ff59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae9d0db6298e4db797315b48cfc32a55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1be0158e98a4d4fb0d4e4b13673d151",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "351c0c03884044ac88e2bd4383a0b950",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e2fc5a55db8430bbf65b7ec1313d5c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00c0a968a88c459eb600079f6ac3d4ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f98f4a9019594d1ca3d368cabdbb3d68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9e656c4b8b64be2993bb5f03da63297",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1120ad5cf6bf46b380d1ac5b35221488",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53fa9c1383bd43abb91425d9f9c4b890",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fed7fa71b0fd47a8b1fe278b7232ea7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07b9835b8e8e49b3aa2b491442808180",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3498e812d0f64c11828f829658a1b5e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81d36a5a0cb3413e8f8f4105c026194b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "136a99f4319f4341bdbeed81dbeaa01c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7cad24201cab403ea248a4b7b41e8cc8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60ecb957978c4b2f96f30e6a7fba32b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb5d2ac5c7fc4a618c1bb1d5b3d775e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43a2bc7301fd49ca8693584fbd0a99fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f08e0ac9d9f34e64a07653ce7e250d6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c183aadb5df04eb2a57c1d91f43cba27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "882b4e18a92e45e18dcd22f14ebd93f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd630d11c5e9459f9c4517e88a5891c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f72281bb3db940a7b3320f518d9c89fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b9a9b4bead44604a5a2a4aa1390b023",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a85aed1a28d34b3e960e590fb0ca18ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e73f99e7a26e4b2696dd4f3b4c050557",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4ac7854b8f745d8a1d78e0920ce7749",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "346f75de03a1472580b2f0513765ea69",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "008bedc0d3284205aeb92ac27cbe5246",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4c199eab4b344e99dfd207a1be077b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7855d6123705475082645d9a7c8051f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "111e21b2c78b47d3b6d05f64d895cf68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de53167dd97f4f54b90e97c67344552f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80732e49c8b64ef4918013812abc4ef5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c19619e8d3524727968313a90b3fc7e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99e28114b4ad4f8dae66f88f659194f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "239f76cdae854faeb4c866eb88d587f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7e5ff4a9a5e46d58f64f17b15f66ca8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf74bb7a025b46a48bfcb75c2c4b1d0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7eafd2a37bcc403e80b86b5c4ba06fce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c21f6dacd056479d9a215565bebe1e5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8929c29a8504d948e319ba683727d6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9afd138d4b09451a9766793f2993cf76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2564a7bcb9f24fabaf590fe0c4a2e884",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d05775d82c14d43806005b130933bf1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec541cc7adcb43599d5cbac88319d54b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18f4161ec04e494e9ab28b2d8db7b437",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91d7a6172a8d47478c0e91e8376c0721",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88b9d1a2fd6c4b14a2f9379771b0c2fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2879f04a29c34158877674e72bd0b88f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "487a9363824c4fd1b1cb5b0efca6c76d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9602e4fa2a446fbb7413cb22f929b84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "name": "stderr",
@@ -374,13 +21395,6 @@
      "text": [
       "`Trainer.fit` stopped: `max_epochs=1500` reached.\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 18.88it/s, v_num=102, val_loss=0.000148, bound_cond_loss=1.44e-6, phys_cond_loss=0.000132, train_loss=0.000134]\n"
-     ]
     }
    ],
    "source": [
@@ -423,17 +21437,22 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'method'>\n"
-     ]
+     "data": {
+      "text/plain": [
+       "{'val_loss': tensor(0.0015),\n",
+       " 'bound_cond_loss': tensor(3.6597e-05),\n",
+       " 'phys_cond_loss': tensor(0.0012),\n",
+       " 'train_loss': tensor(0.0013)}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "# inspecting final loss\n",
-    "trainer.logged_metrics\n",
-    "print(type(problem.truth_solution))"
+    "trainer.logged_metrics"
    ]
   },
   {
@@ -453,7 +21472,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fb2928d33e0>"
+       "<matplotlib.legend.Legend at 0x7f5e63a7d0d0>"
       ]
      },
      "execution_count": 9,
@@ -462,7 +21481,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -496,16 +21515,9 @@
    "id": "bf6211e6",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n"
-     ]
-    },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAGwCAYAAABFFQqPAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAQ65JREFUeJzt3Xl4VOXh/v97ZrLvIYEsZANZw5KwRIjggg3ihktttRQRtWq11GqxVqlXtdZvxbY/W6tG2/JpRVu3ahGruKOCCzuELexLEghJ2JLJQraZ8/sjZCSGQAiTnFner+vKBZk5zNyPYub2Oc95jsUwDEMAAAB+yGp2AAAAALNQhAAAgN+iCAEAAL9FEQIAAH6LIgQAAPwWRQgAAPgtihAAAPBbAWYH8HROp1OlpaWKjIyUxWIxOw4AAOgEwzBUXV2t5ORkWa0dz/tQhE6jtLRUqampZscAAABdUFJSopSUlA6fpwidRmRkpKSWf5BRUVEmpwEAAJ1ht9uVmprq+hzvCEXoNFpPh0VFRVGEAADwMqdb1sJiaQAA4LcoQh3Iz89XZmamcnJyzI4CAAC6iYW7z5+a3W5XdHS0qqqqODUGAICX6OznNzNCAADAb1GEAACA36IIAQAAv0URAgAAfosi1AGuGgMAwPdx1dhpcNUYAADeh6vGAAAAToMiBAAA/BZFCAAA+C2KkElKK49pywG72TEAAPBrFKEOdPdVY3/4YKsuf/oLzVmwQQ3Njm55DwAAcGoUoQ7MmjVLhYWFWrVqldtf2+E05DAkw5BeXVmiP3ywze3vAQAATo8iZAKb1aJnpo1S/g9HS5L+8eUeLd5SbnIqAAD8D0XIRFeMTNKM8emSpJ++sk5rio6anAgAAP9CETLZw1MzdcGg3jrW5NCMf6zQku0HzY4EAIDfoAiZLNBm1fPTR+v8gfGqa3ToR/NX6dWVxWbHAgDAL1CEPEB4cID+MTNHV2cnq9lpaM6CjfrVWxvV2Ow0OxoAAD6NIuQhggKseuqGbN0/ZbAsFumVFcWaNm+5Kuz1ZkcDAMBnUYQ6YMbd5y0Wi2ZNGqB/zsxRZEiA1hQd1ZXPfKlluw73WAYAAPwJd58/DbPuPr/3UK3u+NdqbS+vkcUi/fiCczR78iAFBdBdAQA4He4+7+Uy4sO1cNYE/SAnVYYh/XXJLl33/NfaVlZtdjQAAHwGRciDhQUF6InrRur56aMVHRqojfurdOUzX+jJj7apvonbcgAAcLYoQl7gshFJ+vDeC5Q3NEFNDkPPfLpTlz/9BWuHAAA4SxQhL5EYHaJ5N43Rc9NHq3dksHYfrNW0ect157/WaO+hWrPjAQDglVgsfRpmLZY+lapjTfrjh1v1yopiOQ0p0GbRjPEZuvviAYoNDzI7HgAApuvs5zdF6DQ8sQi12lZWrcff2+K6LUd4kE03T8jQbRP7U4gAAH6NIuQmnlyEWi3ZflC/f3+rCg/YJX1TiH40sb96UYgAAH6IInSW8vPzlZ+fL4fDoe3bt3t0EZIkwzD0cWG5nvpkh6sQhQRa9b0xKbp1Qj/17x1hckIAAHoORchNvGFG6EStheiZT3dq4/4qSZLFIn1nSB/ddn5/jevXSxaLxeSUAAB0L4qQm3hbEWplGIZW7Dmi//titz7ZUuF6fETfaN12fj9dMSJJATYuGgQA+CaKkJt4axE60a6DNfrnl3v05pp9ajh+R/uU2FDdfn5/fX9sisKCAkxOCACAe1GE3MQXilCrI7WN+vfyIr349V4drm2UJMWGBeqm3AzdlJuuuIhgkxMCAOAeFCE38aUi1Kq+yaE31uzTvKW7VXykTlLLwuobxqbqJ5MGKCEqxOSEAACcHYqQm/hiEWrlcBr6YFOZ/rpkl2thdXCAVTflpuvOC89hhggA4LUoQm7iy0WolWEY+nrXYf354+1aXXRUkhQWZNOtE/rp9gv6Kzo00OSEAACcGYqQm/hDEWplGIaWbD+oJz/a7poh6hUepNmTB+kHOalcZQYA8BoUITfxpyLUyjAMfVRYrj9+uE07K2okSYMTIvXrKzM1cWC8yekAADg9ipCb+GMRatXkcOqVFcX68yfbVVnXJEm6JDNBj149TEnRoSanAwCgY539/OZcRwfy8/OVmZmpnJwcs6OYJtBm1czzMvT5Ly7SLRMyFGC16KPCck3+01K9+PVeOZx0aACAd2NG6DT8eUbo27aVVWvOgg1aW1wpScpOjdHc747Q0CT//ucCAPA8zAjB7QYnRurNO8/TY9cMV2RwgApKKnXVs1/q+c93MTsEAPBKFCGcEavVohnj0/Xx7As1OTNBTQ5Dv/9gq6b9fblKjm/OCACAt6AIoUsSo0P09xlj9IfvjVR4kE0r9x7R5X/5Qv9bX2p2NAAAOo0ihC6zWCy6fmyq3r/nAo1Jj1V1Q7N+9uo6PfrOZjU5nGbHAwDgtChCOGtpcWF6/Y7xmjXpHEnSC1/t1bS/L1eFvd7kZAAAnBpFCG4RYLPq/ilDNO+msYoMDtDqoqO68pkvten4DtUAAHgiihDcanJmgv5390QNSohQRXWDbvjbMn22rcLsWAAAnBRFCG7XLz5cb951niYMiFNto0O3vbhar60sNjsWAADtUITQLaJCAvXCzefqu6P7yuE09OCCjfrnl3vMjgUAQBsUIXSboACrnvx+ln58YX9J0m/fLdT/fbHb5FQAAHyDIoRuZbFY9OClQ/TTSQMkSf9v0RbNW0oZAgB4BooQup3FYtF9lwzSvXkDJUm/e2+L3lhdYnIqAAAoQughFotF9+YN0o8vaDlN9uCCjVq8pdzkVAAAf0cRQo964NIhrgXUs15Zyz5DAABTUYTQo6xWi35/3UhdMKi36pucuuOl1TpU02B2LACAn6IIdSA/P1+ZmZnKyckxO4rPCbRZ9cy0UeofH67Sqnr95OW13JsMAGAKi2EYhtkhPJndbld0dLSqqqoUFRVldhyfsrOiWtfkf62ahmbdOqGfHp6aaXYkAICP6OznNzNCMM2APpH68w3ZkqR/frWHW3EAAHocRQimmpyZoJvPy5Ak3f/Geh2sZr0QAKDnUIRgugcvG6IhiZE6VNOoX7yxXpytBQD0FIoQTBcSaNPT00YpOMCqJdsPasHa/WZHAgD4CYoQPMKghEjdc3zn6ccWFXJJPQCgR1CE4DFuP7+/hiZFqbKuSY+9W2h2HACAH6AIwWME2qz6/XUjZLVIbxeUasn2g2ZHAgD4OIoQPMrIlBjNPH4V2WPvFqqZjRYBAN2IIgSPc2/eIMWGBWpnRY1eWVlsdhwAgA+jCMHjRIcGavYlgyVJf/p4u6rqmkxOBADwVRQheKRpOakalBChyromPff5TrPjAAB8FEUIHinAZtWcy4ZKkl5ctlcV1fUmJwIA+CKKEDzWRYN7a1RajOqbnHrus11mxwEA+CCKEDyWxWLRfZNb1gq9sqJYB6qOmZwIAOBrKELwaBMGxOncfr3U6HDq2U9ZKwQAcC+KEDxay6zQIEnSG6v3qcLOWiEAgPtQhODxxvWP05j0WDU6nHrh671mxwEA+BCKELzCjy/oL0n69/Ii1TQ0m5wGAOArKELwCnlDE9S/d7iq65v1GrtNAwDchCIEr2C1WnT7+S2zQv/8co+auAcZAMAN/KIIXXvttYqNjdX3vvc9s6PgLFw7qq/iI4JVWlWv9zYeMDsOAMAH+EURuueee/TSSy+ZHQNnKSTQphvHp0mSXl7O6TEAwNnziyJ00UUXKTIy0uwYcINp56bJZrVo5d4j2lpmNzsOAMDLmV6Eli5dqqlTpyo5OVkWi0ULFy5sd0x+fr4yMjIUEhKicePGaeXKlT0fFB4hISpEU4YlSGq5ggwAgLNhehGqra1VVlaW8vPzT/r866+/rtmzZ+uRRx7R2rVrlZWVpSlTpqiiosJ1THZ2toYPH97uq7S0tKeGgR5047h0SdJba/dzKT0A4KwEmB3gsssu02WXXdbh83/60590++2365ZbbpEk/fWvf9WiRYv0z3/+Uw8++KAkqaCgwG15Ghoa1NDQ4Prebuf0i6fJPSdO/XuHa/fBWr21br9mjE83OxIAwEuZPiN0Ko2NjVqzZo3y8vJcj1mtVuXl5WnZsmXd8p5z585VdHS06ys1NbVb3gddZ7FYXOXnlRUsmgYAdJ1HF6FDhw7J4XAoISGhzeMJCQkqKyvr9Ovk5eXp+9//vt577z2lpKScskTNmTNHVVVVrq+SkpIu50f3uXZUXwXZrNpywK7NpVVmxwEAeCnTT431hE8++aTTxwYHBys4OLgb08AdYsKCNDkzQYs2HtCba/ZpWHK02ZEAAF7Io2eE4uPjZbPZVF5e3ubx8vJyJSYmmpQKnuJ7Y1IkSW8XlKqxmZ2mAQBnzqOLUFBQkMaMGaPFixe7HnM6nVq8eLFyc3O79b3z8/OVmZmpnJycbn0fdN35A+PVJzJYR2ob9dm2itP/AQAAvsX0IlRTU6OCggLXlV979uxRQUGBiotbFsHOnj1b8+bN04svvqgtW7borrvuUm1tresqsu4ya9YsFRYWatWqVd36Pui6AJtV147uK0l6c80+k9MAALyR6WuEVq9erUmTJrm+nz17tiRp5syZmj9/vm644QYdPHhQDz/8sMrKypSdna0PPvig3QJq+KfvjU7R35bs1mdbK3SopkHxEazvAgB0nsUwDMPsEJ7MbrcrOjpaVVVVioqKMjsOTuLq/K+0vqRSv74yUz+a2M/sOAAAD9DZz2/TT40BZ+va7GRJ0rsb2EkcAHBmKEIdYLG097h8ZJKsFmldcaVKjtSZHQcA4EUoQh1gsbT36BMZotxz4iRJ7zArBAA4AxQh+ISpI1tOj72z/oDJSQAA3oQiBJ9w6fBEBVgt2nLArp0V1WbHAQB4CYoQfEJMWJAuGNRbErNCAIDOowh1gMXS3mdqVpKklnVC7AoBAOgMilAHWCztffKGJig4wKrdB2tVeMBudhwAgBegCMFnRIYE6uIhfSRJ727g9BgA4PQoQvApl41oOT324aYyTo8BAE6LIgSfMmlwbwXZrNp9qFY7KmrMjgMA8HAUIfiUyJBAnT8wXpL0/sYyk9MAADwdRagDXDXmvaYMT5QkfbCZIgQAODWKUAe4asx7TR6aINvxzRWLDteaHQcA4MEoQvA5seFBGt+/lyTpg03MCgEAOkYRgk+6dBinxwAAp0cRgk+aMixRFou0rrhSB6qOmR0HAOChKELwSX2iQjQ6LVaS9NHmcpPTAAA8FUUIPuuy41ePvb+JXaYBACdHEYLPmnJ8ndDKPUd0uKbB5DQAAE9EEeoA+wh5v9ReYRreN0pOQ/q4kNNjAID2KEIdYB8h39B69diHXD0GADgJihB82qXH1wl9tfOw7PVNJqcBAHgaihB82oA+kTqnd7gaHU59trXC7DgAAA9DEYLPa50VYpdpAMC3UYTg8y4dliRJ+nzbQR1rdJicBgDgSShC8HnD+0apb0yojjU5tHTHQbPjAAA8CEUIPs9isbj2FPqQ02MAgBNQhOAXWtcJfbKlXI3NTpPTAAA8BUWoA2yo6FvGpMcqPiJI9vpmLd992Ow4AAAPQRHqABsq+hab1aJLjp8e+4DNFQEAx1GE4Ddad5n+aHO5HE7D5DQAAE9AEYLfGN8/TlEhATpU06C1xUfNjgMA8AAUIfiNoACr8oYmSGJzRQBAC4oQ/MqUE3aZNgxOjwGAv6MIwa9cMLC3QgNt2l95TJtL7WbHAQCYjCIEvxIaZNNFg3tL4vQYAIAiBD/kugkrl9EDgN+jCMHvTBrSR4E2i3ZW1GhnRbXZcQAAJqIIwe9EhQRqwoB4SZweAwB/RxGCX2rdXHHRRooQAPgzilAHuNeYb7t0eKICbRZtOWDXjnJOjwGAv6IIdYB7jfm2mLAgXTCw5eqx/60vNTkNAMAsFCH4rauykyW1FCE2VwQA/0QRgt+anJmg0ECbig7Xaf2+KrPjAABMQBGC3woLCtDkzJZ7j/2vgNNjAOCPKELwa1dltZwee3dDqRxOTo8BgL+hCMGvXTCot6JDA1VR3aAVuw+bHQcA0MMoQvBrQQFWXT6iZU+htzk9BgB+hyIEv3d1dl9J0qKNB1TX2GxyGgBAT6IIwe+N69dL6XFhqmlo1vvsNA0AfoUiBL9nsVj0/TEpkqT/rC4xOQ0AoCdRhABJ141JkcUirdhzRHsP1ZodBwDQQyhCgKSk6FDXLTfeXLPP5DQAgJ5CEQKOu35sqqSWIsSeQgDgHyhCwHF5mX0UExaoMnu9lu44aHYcAEAPoAgBxwUH2HTtqJZL6V9eXmRyGgBAT6AIdSA/P1+ZmZnKyckxOwp60I3j0yVJi7dWqORInclpAADdjSLUgVmzZqmwsFCrVq0yOwp60Dm9IzRxQLwMQ3p5RbHZcQAA3YwiBHzLjNyWWaHXVxWrvslhchoAQHeiCAHf8p0hfZQcHaKjdU1atOGA2XEAAN2IIgR8S4DNqunH1wq9xKJpAPBpFCHgJG7ISVWQzar1JZVaW3zU7DgAgG5CEQJOIj4iWFdlJ0uS5i3dbXIaAEB3oQgBHbjjgv6SpA82l3H/MQDwURQhoAODEiI1aXBvGYb0jy/3mB0HANANKELAKdx+fFbojTUlOlLbaHIaAIC7UYSAU8jtH6cRfaNV3+TUS8v2mh0HAOBmFCHgFCwWi2tW6KVlRTrWyAaLAOBLKELAaVw+PFEpsaE6Utuo11Zx2w0A8CUUIeA0AmxW3XXROZKkvy3ZrYZmZoUAwFdQhIBO+N6YFCVGhajMXq831+wzOw4AwE0oQkAnBAfY9OMLW9YKPf/5LjU5nCYnAgC4A0UI6KQf5KQpPiJI+44e09sFpWbHAQC4AUUI6KTQIJtuO79lVui5z3bK4TRMTgQAOFsUIeAM3Dg+XTFhgdp9qFaLNh4wOw4A4CxRhIAzEBEcoFsn9JMk5X+6U05mhQDAq1GEgDM087wMRQYHaFt5tT4qLDc7DgDgLFCEgDMUHRqomedlSJKe/WyHDINZIQDwVl0qQi+++KIWLVrk+v6Xv/ylYmJidN5556moqMht4QBPdevEfgoNtGnTfrs+337Q7DgAgC7qUhF6/PHHFRoaKklatmyZ8vPz9Yc//EHx8fH6+c9/7taAZ6ukpEQXXXSRMjMzNXLkSL3xxhtmR4IP6BUepBvHp0mSnlnMrBAAeKsuFaGSkhINGDBAkrRw4UJdd911uuOOOzR37lx98cUXbg14tgICAvTUU0+psLBQH330ke69917V1taaHQs+4Pbz+ysowKq1xZVatuuw2XEAAF3QpSIUERGhw4dbfvB/9NFHmjx5siQpJCREx44dc186N0hKSlJ2drYkKTExUfHx8Tpy5Ii5oeAT+kSFaFpOqiTpmU93mpwGANAVXSpCkydP1m233abbbrtN27dv1+WXXy5J2rx5szIyMs7otZYuXaqpU6cqOTlZFotFCxcubHdMfn6+MjIyFBISonHjxmnlypVdia01a9bI4XAoNTW1S38e+LY7LjxHgTaLlu0+rNV7KdgA4G26VITy8/OVm5urgwcP6r///a/i4uIktRSNadOmndFr1dbWKisrS/n5+Sd9/vXXX9fs2bP1yCOPaO3atcrKytKUKVNUUVHhOiY7O1vDhw9v91Va+s1tEI4cOaKbbrpJf//730+Zp6GhQXa7vc0X0JG+MaG6bnSKJGaFAMAbWQwPWuVpsVj01ltv6ZprrnE9Nm7cOOXk5OjZZ5+VJDmdTqWmpuruu+/Wgw8+2KnXbWho0OTJk3X77bdrxowZpzz2N7/5jR599NF2j1dVVSkqKqrzg4HfKDpcq4ufXCKH09D/fjpBI1NizI4EAH7PbrcrOjr6tJ/fXZoR+uCDD/Tll1+6vs/Pz1d2drZ++MMf6ujRo115yZNqbGzUmjVrlJeX53rMarUqLy9Py5Yt69RrGIahm2++WRdffPFpS5AkzZkzR1VVVa6vkpKSLueHf0iPC9fVWcmSpGeZFQIAr9KlInT//fe7Thlt3LhR9913ny6//HLt2bNHs2fPdlu4Q4cOyeFwKCEhoc3jCQkJKisr69RrfPXVV3r99de1cOFCZWdnKzs7Wxs3buzw+ODgYEVFRbX5Ak7nJ5POkcUifVRYrq1lnE4FAG8R0JU/tGfPHmVmZkqS/vvf/+rKK6/U448/rrVr17oWTnuKiRMnyul0mh0DPm5An0hdPjxJizYe0LOf7tSzPxxtdiQAQCd0aUYoKChIdXV1kqRPPvlEl1xyiSSpV69ebl1cHB8fL5vNpvLytvdzKi8vV2Jiotve52Ty8/OVmZmpnJycbn0f+I6fXtyyt9aijQe062CNyWkAAJ3RpSI0ceJEzZ49W4899phWrlypK664QpK0fft2paSkuC1cUFCQxowZo8WLF7seczqdWrx4sXJzc932Picza9YsFRYWatWqVd36PvAdQ5OilDc0QYYhPffZLrPjAAA6oUtF6Nlnn1VAQIDefPNNPf/88+rbt68k6f3339ell156Rq9VU1OjgoICFRQUSGo57VZQUKDi4mJJ0uzZszVv3jy9+OKL2rJli+666y7V1tbqlltu6Up0oFu1zgotLNivkiN1JqcBAJyO6ZfPf/7555o0aVK7x2fOnKn58+dLailef/zjH1VWVqbs7Gw9/fTTGjduXI/k6+zld0CrGf9YoS92HNLM3HQ9evVws+MAgF/q7Od3l4uQw+HQwoULtWXLFknSsGHDdNVVV8lms3UtsYeiCOFMfbnjkG78xwqFBtr09YMXKzY8yOxIAOB3unUfoZ07d2ro0KG66aabtGDBAi1YsEA33nijhg0bpl27fGNtBIul0VUTBsRpWHKUjjU59K/lRWbHAQCcQpdmhC6//HIZhqGXX35ZvXr1kiQdPnxYN954o6xWqxYtWuT2oGZhRghd8XbBft3zWoHiwoP01YMXKyTQt2ZKAcDTdeuM0JIlS/SHP/zBVYIkKS4uTk888YSWLFnSlZcEfMoVI5LUNyZUh2sb9eaafWbHAQB0oEtFKDg4WNXV1e0er6mpUVAQ6yGAAJtVP5rYT5L0f1/slsPpMbf0AwCcoEtF6Morr9Qdd9yhFStWyDAMGYah5cuX684779RVV13l7oyAV7ohJ1XRoYHae7hOHxd27pYwAICe1aUi9PTTT+ucc85Rbm6uQkJCFBISovPOO08DBgzQU0895eaIgHcKDw7QjPHpkqS/Ld0tk3eqAACcRJfuNRYTE6O3335bO3fudF0+P3ToUA0YMMCt4cyUn5+v/Px8ORwOs6PAi808L0N//2K31hVXanXRUeVk9Dr9HwIA9JhOXzV2JneV/9Of/tTlQJ6Gq8ZwtuYs2KBXV5Yob2iC/m/mWLPjAIBf6Oznd6dnhNatW9ep4ywWS2dfEvALt53fX6+uLNHireUqOlyr9LhwsyMBAI7rdBH67LPPujMH4LPO6R2hiwb31ufbDmr+13v1yNRhZkcCABzXpcXSAM7MrRNaLqV/Y/U+Vdc3mZwGANCKIgT0gPMHxmtAnwjVNDTrP6vZYBEAPAVFqAPcawzuZLFYdMuEDEnSi1/vZYNFAPAQFKEOzJo1S4WFhVq1apXZUeAjvjsqRdGhgSo+UqfFW8rNjgMAEEUI6DGhQTZNOzdNkvTCV3vNDQMAkEQRAnrUTbnpslktWrb7sApL7WbHAQC/RxECelByTKguHZ4oSXrhqz0mpwEAUISAHtZ6Kf3b60t1qKbB5DQA4N8oQkAPG50Wo6yUaDU2O/XKimKz4wCAX6MIdYDL59FdLBaLbp3YMiv0r+VFanI4TU4EAP6LItQBLp9Hd7pseJJ6RwbrYHWDPtxcZnYcAPBbFCHABEEBVk3LSZUkvbSsyOQ0AOC/KEKASX44ruVS+pV7jmhrGZfSA4AZKEKASRKjQ3RJZoIk6V/MCgGAKShCgIlm5KZLkt5at1927koPAD2OIgSYKLd/nAb2iVBdo0ML1nBXegDoaRQhwEQWi8U1K/Sv5UUyDO5KDwA9iSIEmOzaUX0VHmTTroO1+nrXYbPjAIBfoQh1gA0V0VMiQwL13dEpkqSXlu01NwwA+BmLwVz8KdntdkVHR6uqqkpRUVFmx4GP2l5erUv+vFRWi/TlAxcrOSbU7EgA4NU6+/nNjBDgAQYlRGp8/15yGtKrK7n/GAD0FIoQ4CFuys2QJL26skSNzdx/DAB6AkUI8BCTMxOUEBWsQzUNen/TAbPjAIBfoAgBHiLQZtW0c9MksdM0APQUihDgQX54bpoCrBatLjqqbWXVZscBAJ9HEQI8SJ+oEOUNbbn/2CsrmBUCgO5GEQI8zA/HtZweW7Buv441OkxOAwC+jSIEeJiJA+KV1itM1fXNemdDqdlxAMCnUYQAD2O1WvSDc1MlSa+sYE8hAOhOFCHAA31/TKoCrBYVlFSqsNRudhwA8FkUoQ5wrzGYqXdksKYMS5QkvbKSRdMA0F0oQh2YNWuWCgsLtWrVKrOjwE+1LppeuK5UtQ3NJqcBAN9EEQI8VG7/OGXEhammoVnvrGfRNAB0B4oQ4KGsVotrp+lXuBErAHQLihDgwb43JkVBNqs27KvSpv1VZscBAJ9DEQI8WFxEsKYMb1k0/TKX0gOA21GEAA/3w+Onx/5XsF81LJoGALeiCAEebnz/XurfO1y1jQ69XbDf7DgA4FMoQoCHs1gsrlmhV1YUyzAMkxMBgO+gCAFe4LrRKQoKsGpzqV3r97FoGgDchSIEeIHY8CBdMSJJkvTKCnaaBgB3oQgBXqJ1p+l31h+Qvb7J5DQA4BsoQoCXGJseqwF9InSsyaG317FoGgDcgSIEeIkTF02/zKJpAHALihDgRb47uq+CA6zaWlatgpJKs+MAgNejCAFeJCbsm0XTr3L/MQA4axShDuTn5yszM1M5OTlmRwHaYNE0ALgPRagDs2bNUmFhoVatWmV2FKCNMemxGnh80fRCFk0DwFmhCAFexmKxuGaF2GkaAM4ORQjwQt8dleJaNL2ORdMA0GUUIcALRYcF6oqRxxdNr2DRNAB0FUUI8FLTWxdNbyhV1TEWTQNAV1CEAC81Oi1WgxIiVN/k1NsFLJoGgK6gCAFe6sSdplk0DQBdQxECvNi1o79ZNL22uNLsOADgdShCgBeLDg3UlSOTJbHTNAB0BUUI8HKtewq9y6JpADhjFCHAy41Oi9HghEjVNznZaRoAzhBFCPBy7DQNAF1HEQJ8wDWj+iok0Kpt5SyaBoAzQRECfMCJi6ZfYadpAOg0ihDgI9osmq5j0TQAdAZFCPARo1JjNCQxUg3NTr21bp/ZcQDAK1CEAB/RZtH0ShZNA0BnUIQAH3J1dsui6e3lNVpbfNTsOADg8ShCgA+JDg3U1OOLpl9m0TQAnBZFCPAxrafHFm04wKJpADgNihDgY7JPWDS9gEXTAHBKFCHAx1gsFk1np2kA6BSfL0KVlZUaO3assrOzNXz4cM2bN8/sSEC3u3pUX4UF2bSjokbLdh02Ow4AeCyfL0KRkZFaunSpCgoKtGLFCj3++OM6fJgPBvi2qJBAXTc6RZL0wtd7zQ0DAB7M54uQzWZTWFiYJKmhoUGGYXCqAH5h5nkZkqRPtpSr+HCduWEAwEOZXoSWLl2qqVOnKjk5WRaLRQsXLmx3TH5+vjIyMhQSEqJx48Zp5cqVZ/QelZWVysrKUkpKiu6//37Fx8e7KT3guQb0idAFg3rLMKSXlu01Ow4AeCTTi1Btba2ysrKUn59/0udff/11zZ49W4888ojWrl2rrKwsTZkyRRUVFa5jWtf/fPurtLRUkhQTE6P169drz549euWVV1ReXt5hnoaGBtnt9jZfgLe6ZUKGJOn11SWqbWg2NwwAeCCL4UHniSwWi9566y1dc801rsfGjRunnJwcPfvss5Ikp9Op1NRU3X333XrwwQfP+D1+8pOf6OKLL9b3vve9kz7/m9/8Ro8++mi7x6uqqhQVFXXG7weYyek0lPenJdp9qFaPXT1MM3IzzI4EAD3CbrcrOjr6tJ/fps8InUpjY6PWrFmjvLw812NWq1V5eXlatmxZp16jvLxc1dXVklrKzNKlSzV48OAOj58zZ46qqqpcXyUlJWc3CMBEVqvFtVboha/3yun0mP/vAQCP4NFF6NChQ3I4HEpISGjzeEJCgsrKyjr1GkVFRTr//POVlZWl888/X3fffbdGjBjR4fHBwcGKiopq8wV4s+vGpCgyOEC7D9Zq6Y6DZscBAI8SYHaA7nbuueeqoKDA7BiAaSKCA/T9san651d79MJXe3XR4D5mRwIAj+HRM0Lx8fGy2WztFjeXl5crMTHRpFSA95l5XrqsFmnJ9oPacoALAACglUcXoaCgII0ZM0aLFy92PeZ0OrV48WLl5uZ263vn5+crMzNTOTk53fo+QE9IjwvXZSOSJEl/W7LL5DQA4DlML0I1NTUqKChwnb7as2ePCgoKVFxcLEmaPXu25s2bpxdffFFbtmzRXXfdpdraWt1yyy3dmmvWrFkqLCzUqlWruvV9gJ5y14XnSJLe2XBAJUfYYBEAJA9YI7R69WpNmjTJ9f3s2bMlSTNnztT8+fN1ww036ODBg3r44YdVVlam7OxsffDBB+0WUAM4teF9o3X+wHh9seOQ5n2xW7+9erjZkQDAdB61j5An6uw+BIA3+HrXIf1w3goFB1j11YMXKz4i2OxIANAtfGIfIQDulds/TlmpMWpoduqFr/aYHQcATEcR6gCLpeGLLBaLa63QS18XqbKu0eREAGAuilAHWCwNX3VJZoKGJEaquqFZ877YbXYcADAVRQjwM1arRbMnD5IkvfDVXh2uaTA5EQCYhyIE+KHJmQkamRKtukaH/sq+QgD8GEUI8EMWyzezQi8tK1K5vd7kRABgDopQB1gsDV934aDeGpseq4Zmp/744Taz4wCAKdhH6DTYRwi+bF3xUV373NeSpHd+OlEjUqJNTgQA7sE+QgBOa1RarK4d1VeS9Og7m8X/FwHwNxQhwM/98tLBCgm0anXRUb274YDZcQCgR1GEAD+XFB2qO49vsvj/FhWq6liTyYkAoOdQhADozgvPUb/4cJXbG/TE+1vMjgMAPYYi1AGuGoM/CQm0ae53R0iSXl1ZomW7DpucCAB6BleNnQZXjcGf/OqtjXplRbFSYkO16GfnKzo00OxIANAlXDUG4IzNuWyIUnuFat/RY3rwvxu4igyAz6MIAXCJDAnUs9NGK9Bm0fubyvSv5UVmRwKAbkURAtBGVmqMHrh0iCTpsXcL9fWuQyYnAoDuQxEC0M6PJvbTlSOT1OQwdOe/1mhnRbXZkQCgW1CEALRjsVj0/30/S2PSY2Wvb9b0/1uhPYdqzY4FAG5HEQJwUiGBNv19xhgN7BOhcnuDfvD3ZcwMAfA5FKEOsI8QIMVFBOvVO8ZrUEJLGbo2/2t9vq3C7FgA4DbsI3Qa7CMESEdqG/Xjf63Wqr1HZbW07ER9T95ABQfYzI4GACfFPkIA3KZXeJD+fds4/SAnVU5Deu7zXZr6zJf6bFsFew0B8GrMCJ0GM0JAWx9sOqCH3tqkw7WNkqQx6bG6KTddU4YlKiSw52aInE5D1Q3Nsh9rUtWxJtU2NEuSTvyBZlHLWqfQIJtCAmwKCbIqJNCm8KAA2ayWHssKoOd19vObInQaFCGgvcq6RuV/tlMvfl2kRodTkhQWZNPEAfEa3z9OQ5OiNCghQr3Cg2SxdFw4HE7DVWSqjjXJXv/N712PHWv/WFVdk6obmnU2P72iQgIUGx6kmLAgxYYFKiY0UPERwUqMDlFSdOjxX0PUJzJYATYmzwFvQxFyE4oQ0LFye71eW1mi11YV60BVfbvnA6wWxUUEKSTQJpvVIpvFovpmh+oaHKptbFZ9k/OsMwQHWBUdGqjw4AC1dq7W6uU0pIYmh+qbnapvcuhYk+OMy5PVIvWODFZSdKgy4sKUHheujPjjv8aFKzYs8JRlD4A5KEJuQhECTs8wDG0uteuzrRXasL9KWw7Yte/osU7/+bAgm6JDAxUdGqiokEBFHf/9N18Big5r+1jrcWdyOs4wDDU6nKqpb9bRuiZV1jXqaF2TjtY1qrKuUYdqGnWgql5lVcd0oKpe5fZ6NTlO/SMyMiRAGXHhGtAnQgP6RGhQQqQG9olQaq8wTr8BJqIIuQlFCOiahmaHDtc06nBNoxodTjU7nHI4DQUH2hQe3LJOJyzIpsiQQAUFeOapJ6fT0OHaRpVV1Wvf0ToVHalT0eFa7T3U8mvpSWbBWgUHWDWgT4QG9onQwIRIDUmMVGZylBKjQphBAnoARchNKEIAOlLf5FDJkTrtPlSrnRU12l5erR3lNdp5sEaNzSc/7RcbFqjM5CgNS45WZlKUMpOj1D8+nHVIgJtRhM5Sfn6+8vPz5XA4tH37dooQgE5zOA0VH6nT9vJqV0HaeqBaOw/WyOFs/yM3OMDqmjHKTI7WyL7RGpIUyT5NwFmgCLkJM0IA3KW+yaHt5dUqLLWr8IBdhaV2bTlgV22jo92xgTaLhiZFaUTfaGWlxGhkarQG9I5g5gjoJIqQm1CEAHQnp9NQ0ZE6FZbatbm0SptK7dqwr1KVdU3tjg0NtGlYcpRGpsQoKzVaI/pGKyMuXFYWZQPtUITchCIEoKcZhqF9R49p/b5KbdhXpQ37KrVpv101xzeNPFFkSICyUmKUnRqjUWktv8ZFBJuQGvAsFCE3oQgB8AROp6Hdh2q0vqRKG/dXaf2+Sm0utZ90UXZqr1CNSo1VdmqMstNiNCw5ivVG8DsUITehCAHwVE0Op7aVVWv9vkqtK65UQUmldlbUtDsuyGbV0OQojTo+azQ6LVYpsaFcxg+fRhFyE4oQAG9SdaxJG/ZVqqC4UutKWsrRkeP3hTtR78hgjUmL1ej0lmI0vG90j94rDuhuFCE3oQgB8GaGYajkyDGtKzmqdcWVWld8VJtL7Wr+1mX8gTaLhiVHa3RarMaktxSkpOhQk1IDZ48i5CYUIQC+pr7JoQ37qrS2+KjWFh3V2uKjOlTTftYoKTpEo9NiNTo9VqPTYjQsOdpjdwEHvo0i5CYUIQC+rnXWaG1xSylaU3RUW8uq223+GBRg1ci+0a5iNDotVn2iQkxKDZwaRchNKEIA/FFdY7PWl7SdNTp6kr2NUmJDW2aN0mI0Jr2XhiRFKpBNH+EBKEJniVtsAMA3DMPQ3sN1WnO8FK0tOqrt5dX69h1DQgKtGpkS07LO6HhBYl8jmIEi5CbMCAHAyVXXN30za3S8HNnr22/6mBEXptFpsRp1/JTa4IRIbhWCbkcRchOKEAB0Tuumj2uLKl1rjXacZF+j8CCbslJjXFeojUqLUUxYkAmJ4csoQm5CEQKArqs61qSCkkqtKTqqdcVHVVBcqeqT3Cqkf+/w4/satZxSG9gngnuo4axQhNyEIgQA7uNwGtpZUfPNWqPio9p9sLbdcZHBAco6vhN2yz3UYtUrnFkjdB5FyE0oQgDQvY7WNmpdyVHXKbWCkkrVNTraHZcRF6ZRaS2n0kalxnKFGk6JIuQmFCEA6FnNDqe2l9e02Q1710lmjYIDrBqZEt1SjlJjNCotVonR7GuEFhQhN6EIAYD5quqaVLCvpRQVlLTcZLbqWPt9jZKiQ5TtOqUWqxHcQ81vUYTchCIEAJ7HMAztOVTbMmN0fOboZLthB1gtGpoU5VprNCo1VulxYbJYWIjt6yhCbkIRAgDvUNfYrI37qrSupGXmaG1xpQ5WN7Q7LjYssM3ptJGp0YoKCTQhMboTRchNKEIA4J0Mw1BpVb3WFX+z1mjTfrsaHc42x1ks0sA+ERqVGqvs4zNHA/tEysbl+16NIuQmFCEA8B0NzQ5tOVD9TTkqOaqSI8faHde66WPr6bTstBjFc6sQr0IRchOKEAD4toPVDccXYLcsxF5fUqnak1y+nxwdopEpMRqZGq2RfWM0IiVa0aGcUvNUFCE3oQgBgH9xOA3tqKh2nU5bW1ypXQdrdLJPy37x4RrRN1ojU6KVlRqjYclRCgsK6PnQaIci5CYUIQBAdX2TNu23a+P+Sq3fV6UN+ypPekrNapEG9onUyJRojUyN0ci+0RqSFKngAC7h72kUITehCAEATuZobaM27K/ShpLKll/3Varc3v4qtUBbyyX8I/pGK+v4qbUBvSMUwK7Y3Yoi5CYUIQBAZ5Xb67Xh+IxR68xRZV37jR9DA20alhzVsuYoJVojUqLVLy6cG826EUXoLOXn5ys/P18Oh0Pbt2+nCAEAzphhGNp39JjW76t0FaRN++2qaWhud2xYkE2ZSVEalhylYcnRGtY3SgP7RCoogJmjrqAIuQkzQgAAd3I6De0+VHO8GFVp/b5KbTlgV32Ts92xgTaLBiVEalhylIb3jdaw5CgNTWJBdmdQhNyEIgQA6G7NDqf2HKrV5lK7Nu2v0uZSuzaXVsle337myGKR+seHt8wanVCQYsKCTEjuuShCbkIRAgCYofW02ubS1mLUUpIqTnLbEEnqGxOqzOQo16xRZlKUUmJD/fa+ahQhN6EIAQA8ycHqhhPKUcuvRYfrTnpsRHCAhiRGakhSpIYmtRSkwQmRCg/2/VNrFCE3oQgBADydvb5JhcdnjQpL7dpaZteO8pp291WTWk6tpfcK05DEKA1OjNTgxEgNSohURlyYT13STxFyE4oQAMAbNTmc2n2wVlvL7Co8YNfWA9XacsDe4am1IJtV5/SJ0KCECA1KiNTghJaClBIb6pWX9VOE3IQiBADwJYdrGrS1rKUUbS+v1rbyGu0or1bdSe6vJrXsedRajgYlRGpQYktJSogK9uj1RxQhN6EIAQB8ndNpaH/lsePFqFrby1oK0q6DNWpsbn96TZKiQgI0KCFSAxMidU7vcJ3TJ0IDekeob4xnzCBRhNyEIgQA8FfNDqeKjtQdL0bV2lFeo23l1dpzqFYO58nrQ3CAVf17R7SUo94ROqdPy+/7x0coNKjn7rlGEXITihAAAG01NDu0+2CttpdXa2dFy8zRropa7TlUe9IF2q36xoS6itGAPhEtRal3hOIjgtx+mo0i5CYUIQAAOsfhNLTvaJ2rGLWWpJ0Ha056z7VWz0wbpalZyW7N0tnPb9/fSAAAAPQIm9Wi9LhwpceF6+IhbZ87Utt4vCAdn0E6WKtdB2tUcqRO/eLDzQksihAAAOgBvcKD1Cu8l3IyerV5vL7JoUAT9y+iCAEAANOEBPbcAuqT8Z0tJAEAAM4QRQgAAPgtihAAAPBbFCEAAOC3KEIAAMBvUYQAAIDfoggBAAC/5TdFqK6uTunp6frFL35hdhQAAOAh/KYI/e53v9P48ePNjgEAADyIXxShHTt2aOvWrbrsssvMjgIAADyI6UVo6dKlmjp1qpKTk2WxWLRw4cJ2x+Tn5ysjI0MhISEaN26cVq5ceUbv8Ytf/EJz5851U2IAAOArTC9CtbW1ysrKUn5+/kmff/311zV79mw98sgjWrt2rbKysjRlyhRVVFS4jsnOztbw4cPbfZWWlurtt9/WoEGDNGjQoJ4aEgAA8BIWwzAMs0O0slgseuutt3TNNde4Hhs3bpxycnL07LPPSpKcTqdSU1N1991368EHHzzta86ZM0f//ve/ZbPZVFNTo6amJt133316+OGHT3p8Q0ODGhoaXN/b7XalpqaqqqpKUVFRZzdAAADQI+x2u6Kjo0/7+e3Rd59vbGzUmjVrNGfOHNdjVqtVeXl5WrZsWadeY+7cua7TYvPnz9emTZs6LEGtxz/66KPtHrfb7WeYHgAAmKX1c/t08z0eXYQOHTokh8OhhISENo8nJCRo69at3fKec+bM0ezZs13f79+/X5mZmUpNTe2W9wMAAN2nurpa0dHRHT7v0UXI3W6++ebTHhMcHKzg4GDX9xERESopKVFkZKQsFovbsrSecispKfGbU27+NmbG69sYr+/ztzH72ngNw1B1dbWSk5NPeZxHF6H4+HjZbDaVl5e3eby8vFyJiYk9ksFqtSolJaXbXj8qKson/sKdCX8bM+P1bYzX9/nbmH1pvKeaCWpl+lVjpxIUFKQxY8Zo8eLFrsecTqcWL16s3NxcE5MBAABfYPqMUE1NjXbu3On6fs+ePSooKFCvXr2Ulpam2bNna+bMmRo7dqzOPfdcPfXUU6qtrdUtt9xiYmoAAOALTC9Cq1ev1qRJk1zfty5UnjlzpubPn68bbrhBBw8e1MMPP6yysjJlZ2frgw8+aLeA2tsEBwfrkUceabMeydf525gZr29jvL7P38bsb+Nt5VH7CAEAAPQkj14jBAAA0J0oQgAAwG9RhAAAgN+iCAEAAL9FETJJfn6+MjIyFBISonHjxmnlypVmRzpjc+fOVU5OjiIjI9WnTx9dc8012rZtW5tj6uvrNWvWLMXFxSkiIkLXXXdduw0yi4uLdcUVVygsLEx9+vTR/fffr+bm5p4cSpc88cQTslgsuvfee12P+eJ49+/frxtvvFFxcXEKDQ3ViBEjtHr1atfzhmHo4YcfVlJSkkJDQ5WXl6cdO3a0eY0jR45o+vTpioqKUkxMjH70ox+ppqamp4dyWg6HQ7/+9a/Vr18/hYaG6pxzztFjjz3W5l5F3jzepUuXaurUqUpOTpbFYtHChQvbPO+usW3YsEHnn3++QkJClJqaqj/84Q/dPbQOnWrMTU1NeuCBBzRixAiFh4crOTlZN910k0pLS9u8hjeN+XT/jk905513ymKx6KmnnmrzuDeN1y0M9LjXXnvNCAoKMv75z38amzdvNm6//XYjJibGKC8vNzvaGZkyZYrxwgsvGJs2bTIKCgqMyy+/3EhLSzNqampcx9x5551GamqqsXjxYmP16tXG+PHjjfPOO8/1fHNzszF8+HAjLy/PWLdunfHee+8Z8fHxxpw5c8wYUqetXLnSyMjIMEaOHGncc889rsd9bbxHjhwx0tPTjZtvvtlYsWKFsXv3buPDDz80du7c6TrmiSeeMKKjo42FCxca69evN6666iqjX79+xrFjx1zHXHrppUZWVpaxfPly44svvjAGDBhgTJs2zYwhndLvfvc7Iy4uznj33XeNPXv2GG+88YYRERFh/OUvf3Ed483jfe+994yHHnrIWLBggSHJeOutt9o8746xVVVVGQkJCcb06dONTZs2Ga+++qoRGhpq/O1vf+upYbZxqjFXVlYaeXl5xuuvv25s3brVWLZsmXHuuecaY8aMafMa3jTm0/07brVgwQIjKyvLSE5ONv785z+3ec6bxusOFCETnHvuucasWbNc3zscDiM5OdmYO3euianOXkVFhSHJWLJkiWEYLT9kAgMDjTfeeMN1zJYtWwxJxrJlywzDaPmP1mq1GmVlZa5jnn/+eSMqKspoaGjo2QF0UnV1tTFw4EDj448/Ni688EJXEfLF8T7wwAPGxIkTO3ze6XQaiYmJxh//+EfXY5WVlUZwcLDx6quvGoZhGIWFhYYkY9WqVa5j3n//fcNisRj79+/vvvBdcMUVVxi33nprm8e++93vGtOnTzcMw7fG++0PSXeN7bnnnjNiY2Pb/H1+4IEHjMGDB3fziE7vVMWg1cqVKw1JRlFRkWEY3j3mjsa7b98+o2/fvsamTZuM9PT0NkXIm8fbVZwa62GNjY1as2aN8vLyXI9ZrVbl5eVp2bJlJiY7e1VVVZKkXr16SZLWrFmjpqamNmMdMmSI0tLSXGNdtmyZRowY0WaDzClTpshut2vz5s09mL7zZs2apSuuuKLNuCTfHO///vc/jR07Vt///vfVp08fjRo1SvPmzXM9v2fPHpWVlbUZc3R0tMaNG9dmzDExMRo7dqzrmLy8PFmtVq1YsaLnBtMJ5513nhYvXqzt27dLktavX68vv/xSl112mSTfG++J3DW2ZcuW6YILLlBQUJDrmClTpmjbtm06evRoD42m66qqqmSxWBQTEyPJ98bsdDo1Y8YM3X///Ro2bFi7531tvJ1BEephhw4dksPhaLczdkJCgsrKykxKdfacTqfuvfdeTZgwQcOHD5cklZWVKSgoyPUDpdWJYy0rKzvpP4vW5zzNa6+9prVr12ru3LntnvPF8e7evVvPP/+8Bg4cqA8//FB33XWXfvazn+nFF1+U9E3mU/19LisrU58+fdo8HxAQoF69enncmB988EH94Ac/0JAhQxQYGKhRo0bp3nvv1fTp0yX53nhP5K6xedvf8RPV19frgQce0LRp01w3HfW1Mf/+979XQECAfvazn530eV8bb2eYfosN+IZZs2Zp06ZN+vLLL82O0m1KSkp0zz336OOPP1ZISIjZcXqE0+nU2LFj9fjjj0uSRo0apU2bNumvf/2rZs6caXI69/vPf/6jl19+Wa+88oqGDRumgoIC3XvvvUpOTvbJ8eIbTU1Nuv7662UYhp5//nmz43SLNWvW6C9/+YvWrl0ri8VidhyPwYxQD4uPj5fNZmt3JVF5ebkSExNNSnV2fvrTn+rdd9/VZ599ppSUFNfjiYmJamxsVGVlZZvjTxxrYmLiSf9ZtD7nSdasWaOKigqNHj1aAQEBCggI0JIlS/T0008rICBACQkJPjVeSUpKSlJmZmabx4YOHari4mJJ32Q+1d/nxMREVVRUtHm+ublZR44c8bgx33///a5ZoREjRmjGjBn6+c9/7poB9LXxnshdY/O2v+PSNyWoqKhIH3/8sWs2SPKtMX/xxReqqKhQWlqa62dYUVGR7rvvPmVkZEjyrfF2FkWohwUFBWnMmDFavHix6zGn06nFixcrNzfXxGRnzjAM/fSnP9Vbb72lTz/9VP369Wvz/JgxYxQYGNhmrNu2bVNxcbFrrLm5udq4cWOb//BafxB9+wPYbN/5zne0ceNGFRQUuL7Gjh2r6dOnu37vS+OVpAkTJrTbEmH79u1KT0+XJPXr10+JiYltxmy327VixYo2Y66srNSaNWtcx3z66adyOp0aN25cD4yi8+rq6mS1tv2xaLPZ5HQ6JfneeE/krrHl5uZq6dKlampqch3z8ccfa/DgwYqNje2h0XReawnasWOHPvnkE8XFxbV53pfGPGPGDG3YsKHNz7Dk5GTdf//9+vDDDyX51ng7zezV2v7otddeM4KDg4358+cbhYWFxh133GHExMS0uZLIG9x1111GdHS08fnnnxsHDhxwfdXV1bmOufPOO420tDTj008/NVavXm3k5uYaubm5rudbLye/5JJLjIKCAuODDz4wevfu7bGXk3/biVeNGYbvjXflypVGQECA8bvf/c7YsWOH8fLLLxthYWHGv//9b9cxTzzxhBETE2O8/fbbxoYNG4yrr776pJdcjxo1ylixYoXx5ZdfGgMHDvSIy8m/bebMmUbfvn1dl88vWLDAiI+PN375y1+6jvHm8VZXVxvr1q0z1q1bZ0gy/vSnPxnr1q1zXSHljrFVVlYaCQkJxowZM4xNmzYZr732mhEWFmbapdWnGnNjY6Nx1VVXGSkpKUZBQUGbn2MnXhHlTWM+3b/jb/v2VWOG4V3jdQeKkEmeeeYZIy0tzQgKCjLOPfdcY/ny5WZHOmOSTvr1wgsvuI45duyY8ZOf/MSIjY01wsLCjGuvvdY4cOBAm9fZu3evcdlllxmhoaFGfHy8cd999xlNTU09PJqu+XYR8sXxvvPOO8bw4cON4OBgY8iQIcbf//73Ns87nU7j17/+tZGQkGAEBwcb3/nOd4xt27a1Oebw4cPGtGnTjIiICCMqKsq45ZZbjOrq6p4cRqfY7XbjnnvuMdLS0oyQkBCjf//+xkMPPdTmQ9Gbx/vZZ5+d9L/ZmTNnGobhvrGtX7/emDhxohEcHGz07dvXeOKJJ3pqiO2casx79uzp8OfYZ5995noNbxrz6f4df9vJipA3jdcdLIZxwpapAAAAfoQ1QgAAwG9RhAAAgN+iCAEAAL9FEQIAAH6LIgQAAPwWRQgAAPgtihAAAPBbFCEAAOC3KEIAcAY+//xzWSyWdjfXBeCdKEIAAMBvUYQAAIDfoggB8CpOp1Nz585Vv379FBoaqqysLL355puSvjlttWjRIo0cOVIhISEaP368Nm3a1OY1/vvf/2rYsGEKDg5WRkaGnnzyyTbPNzQ06IEHHlBqaqqCg4M1YMAA/eMf/2hzzJo1azR27FiFhYXpvPPO07Zt27p34AC6BUUIgFeZO3euXnrpJf31r3/V5s2b9fOf/1w33nijlixZ4jrm/vvv15NPPqlVq1apd+/emjp1qpqamiS1FJjrr79eP/jBD7Rx40b95je/0a9//WvNnz/f9edvuukmvfrqq3r66ae1ZcsW/e1vf1NERESbHA899JCefPJJrV69WgEBAbr11lt7ZPwA3Iu7zwPwGg0NDerVq5c++eQT5ebmuh6/7bbbVFdXpzvuuEOTJk3Sa6+9phtuuEGSdOTIEaWkpGj+/Pm6/vrrNX36dB08eFAfffSR68//8pe/1KJFi7R582Zt375dgwcP1scff6y8vLx2GT7//HNNmjRJn3zyib7zne9Ikt577z1dccUVOnbsmEJCQrr5nwIAd2JGCIDX2Llzp+rq6jR58mRFRES4vl566SXt2rXLddyJJalXr14aPHiwtmzZIknasmWLJkyY0OZ1J0yYoB07dsjhcKigoEA2m00XXnjhKbOMHDnS9fukpCRJUkVFxVmPEUDPCjA7AAB0Vk1NjSRp0aJF6tu3b5vngoOD25ShrgoNDe3UcYGBga7fWywWSS3rlwB4F2aEAHiNzMxMBQcHq7i4WAMGDGjzlZqa6jpu+fLlrt8fPXpU27dv19ChQyVJQ4cO1VdffdXmdb/66isNGjRINptNI0aMkNPpbLPmCIDvYkYIgNeIjIzUL37xC/385z+X0+nUxIkTVVVVpa+++kpRUVFKT0+XJP32t79VXFycEhIS9NBDDyk+Pl7XXHONJOm+++5TTk6OHnvsMd1www1atmyZnn32WT333HOSpIyMDM2cOVO33nqrnn76aWVlZamoqEgVFRW6/vrrzRo6gG5CEQLgVR577DH17t1bc+fO1e7duxUTE6PRo0frV7/6levU1BNPPKF77rlHO3bsUHZ2tt555x0FBQVJkkaPHq3//Oc/evjhh/XYY48pKSlJv/3tb3XzzTe73uP555/Xr371K/3kJz/R4cOHlZaWpl/96ldmDBdAN+OqMQA+o/WKrqNHjyomJsbsOAC8AGuEAACA36IIAQAAv8WpMQAA4LeYEQIAAH6LIgQAAPwWRQgAAPgtihAAAPBbFCEAAOC3KEIAAMBvUYQAAIDfoggBAAC/9f8DBkdXbJZxIMEAAAAASUVORK5CYII=",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -519,7 +21531,6 @@
     "            idx for idx, s in enumerate(trainer.callbacks)\n",
     "            if isinstance(s, MetricTracker)\n",
     "        ]\n",
-    "print(list_[0])\n",
     "trainer_metrics = trainer.callbacks[list_[0]].metrics\n",
     "\n",
     "loss = trainer_metrics['val_loss']\n",
@@ -574,7 +21585,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/tutorial1/tutorial.py b/tutorials/tutorial1/tutorial.py
index a12dca3..9569611 100644
--- a/tutorials/tutorial1/tutorial.py
+++ b/tutorials/tutorial1/tutorial.py
@@ -38,7 +38,7 @@
 # 
 # ```python
 # from pina.problem import SpatialProblem
-# from pina.geometry import CartesianProblem
+# from pina.domain import CartesianProblem
 # 
 # class SimpleODE(SpatialProblem):
 #     
@@ -233,7 +233,6 @@ trainer.train()
 
 # inspecting final loss
 trainer.logged_metrics
-print(type(problem.truth_solution))
 
 
 # By using `matplotlib` we can also do some qualitative plots of the solution. 
@@ -260,7 +259,6 @@ list_ = [
             idx for idx, s in enumerate(trainer.callbacks)
             if isinstance(s, MetricTracker)
         ]
-print(list_[0])
 trainer_metrics = trainer.callbacks[list_[0]].metrics
 
 loss = trainer_metrics['val_loss']
diff --git a/tutorials/tutorial3/tutorial.ipynb b/tutorials/tutorial3/tutorial.ipynb
index a396482..ddad985 100644
--- a/tutorials/tutorial3/tutorial.ipynb
+++ b/tutorials/tutorial3/tutorial.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 1,
    "id": "d93daba0",
    "metadata": {},
    "outputs": [],
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "id": "b60176c4",
    "metadata": {},
    "outputs": [],
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 3,
    "id": "9fbbb74f",
    "metadata": {},
    "outputs": [],
@@ -181,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "id": "0be8e7f5",
    "metadata": {},
    "outputs": [
@@ -195,18 +195,14032 @@
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00, 17.47it/s, v_num=18]           \n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76717f5a37704e30bb3f1a38e7003ea1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 10.66it/s, v_num=18, val_loss=0.172, bound_cond1_loss=0.000, bound_cond2_loss=0.000, bound_cond3_loss=0.000, bound_cond4_loss=0.000, time_cond_loss=0.143, phys_cond_loss=0.0252, train_loss=0.169] "
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0097048c29dd4be39a547512d9b169e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48d42d6a9b8d46b5b53bc2787d3c51a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7edc4f22087241bd9e203d5a897878c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "748747f9d9a84086876d753cf9add48b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60e94c2b432a4d4d9bb851ca3a53671c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27e15be1112340328cb3b9180f265cb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c82f36a3fa5b4b1583f04ca2ade49eea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "892ec957ee934d21b27d68ede07d0424",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32d728ae63fe47b5abf9e2f796b96f8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a38125e102743eda12aea607f729565",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19fdace17cb94c289e3477a85f025a4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d7ed85ddf0b423ca2f76ae8609c745e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d1b7f7f218846e6b821f51b6379a323",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7824ffed81ab4438812c0799156d19ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbaa510a257d4a4f8021bf48d7ebe85e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc0bf1a78609415299ae54faeb2725a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98a54c51d8d340099901048ee8f980ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee250d65aae9474a9dad32bb06fe84fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e40cac2d5b774dd383806515be187966",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab850d51dce24f41a09b1056e8727437",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef69f63f0b8e43f784739c1348f1f5ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "212fc9c3ec9e43ef819bc6c407bc793d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5214e13a756485f89b88917f54f6ccb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d1204ceb37c42b7ba5bdfc60c1549b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70193394b4e54826908cb1797bbfc5a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "037864a4ea67484eb6b14d2ff79dc433",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5e8d75e6fd841faae207e23164a2fc5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02be027f724f4fb69f48910bff90691f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b6aa475f16941028a64580bf0a6236a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3883dc4bf5f1432997ab48cb1cbe91c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7795f4d1a4e4768afc4f5b036c84420",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97ac4b9a15ff4b1cba6e527c6a242273",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d64496757e5c41dd935e9ee72cd18280",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca7c84114ad043e6ada2c09288f6e8f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d674c67100774c23b7267e7975640dcb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15815ce7892c4f8cafc1703c373b44b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9819f6fa9f784ad7b102fcf29a0f9ca1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cddc4825ba55464684196fb50549db7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89033769755f45a5b2d2c1bc47b630c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a26611ad177746cdb1d1aab3b4165e5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12db8b6374584bbdaac5412fb04a6c8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cf1ddaeca0e49b6af094d717f3d80b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "115b25a8d065427b83aa3c2986dbe069",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5824c0b92890422893679091e5860528",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2508306cd93d4b8f9a199184f291172b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7391bb83e79e43dfa05ef57b70565116",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20148ae865f44e36bdfc8e521b1518f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a095b11915de4f568348c917a65198df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "424c3575b78b4d54a3c86d9f08e6fdbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54c1944234684f0b803365c313c13858",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afcc6e88e29c498d9f0c1c0db2413a4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "958738ddf9904732b0ee7486e85c5bab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14dce2982150499ba790da566d99564a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c339aa1b55334b9ea85ee94e0848a93c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c8949524ce3408497fb7fb218057bc8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba159ed599554a659ec48c555499583a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22be79d2994c4d5da0e2993e36f8916e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a16b698623b4a40bf5742bb12363d88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78324b2cd5c94e399995f7b1621b5840",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46cbc6c6a48344259d9a6d0127c7fc1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6ad1cca6fae4646bc86287d9656f7b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91acf121f2544a47b8614e70e6c9cf5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b81e05abfa4a42f39e5a8dfcdb1dfbb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d318076bbea4f1c858a068e78a6ca87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1c162683c004ddd94b8fc20cd360679",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b43cc82e84747b09cb4de43812b8f82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c2ce7f0c35444a7aab680f3359bb1e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e09cb853564f4173b889872f25de0b20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f178c1816a894bfcbbc07eab8d71fc92",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebe8b7e8cc034af3b8155417194c0924",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae7d81f8bdff417fb74bd0b6af931a58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b6f1427c9ba4c90b03876e66fda9f34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "282da1f784404ed1bba81905f3a81bb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7dafbd5f7d384c49b64f5283c3aa6356",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9768b76f248e4075969a34334273adf6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "296407d103204c2595d3b722ab1d1ef5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27fc2e7297924cd6bf7e25667f7957bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1bf84b409c2043bd81a506f4b4fa4d85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28988803bdd641348315a98cf9074ff3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2113f63e4196424b9f46f724327d2cae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6687c8d3658245bc8b4d82ce7e0672c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b89a3a7cfca4447db419ed536b1b9fc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8deaec5198942bbb21f2a0b7e000e33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82155af980b546b1abb5494a985d1f82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76e083c7ad5a4a3ba76fab0c07175993",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc9a6e1a6d8047d9956842f91de65a0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab09c54691694c858a03ec57663a0df4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94b9a8a5231147fdacf23ab4c4417195",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eadcde1919fa4e558d8b9ffe2cf50faf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f7386adc2464575a69f6c0077a9e62a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ab31275e6fc4d23852cb65083b42094",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47dfa6aa87ee47c7ace6c252a1e1c708",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6324e0c199e843c68455ea212f15f1db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9d012d9a3a84c50ae6d07e3f528676f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adb2211bbc8143db8e457b9e7a5a52a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2aada9410cd9474686a4bdf04b361b92",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "104bc974638d438b8507540b70f08d66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0be0e5967f744c7beb0867e40d7a571",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b2c3216dee449a4a71c9cd083f8427b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c8e8a7d76834babab88f4fb10753e47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a17f4277dc65411e85c5da1789914e76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b819f5a9fa58478583836f420472fc86",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae219697a0d44077a4203ad54cc6e50d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "053a51e5105d4cab89b6ff1cdc08025b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4b842ab3fdb4b17b57f8c1f937c8056",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c9291ab000f4de2bcd0f86b89a57f87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c80c03a61d641829ae0a81d47151948",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5a404460e4e427ea5c572bdcbe0fcf7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4fae0ac725b4059aadffd3fd3634a0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ea3c8ddf3704a74870b573a38e47423",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be1522a13b904a1890667eb928e59aab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7edf148af2441669ea8c92af13f48dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84f59f3e8c7f41e898ad5f86b2507443",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71dbbe8c085e405bbc396961aa8a4e8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e6ddb34c3d214fa6ad7dd01164b75ef0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbb8b02bc1ef4f6281bb11f1688753d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82563854e8014568a36382d53605fcbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de25821dd7bf498d956197c6be18afb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa4a91456ca4433daa8f44372be6fce3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "901a9d8bff90475096d10bdca4f142b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0082865f3d641d1a8c856a07ecc5d58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1c60eff23ad45ae8c5d199f680ffca3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f98337f888e4eafaec01d2c44523ef4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a538e00a00c45af94d5b97fba5985bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7677d88da31e4750b3d789b658e31e68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ba4e84ad1ca47a49c4044550998c1ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7571b260e139475f850c6a0f40087238",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94b16ccb456d45b0b4f66e2f245c6866",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93a92afa7af44ae1b077f66c8cfc522d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "336da4ab72d64e65bc746797b3536a05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21aa656a0aff4e91ab6c65f4b3ad3b6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58b413b35010476fa28c4a089a37c711",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43ad13aae1a7466082ca12c8999c26a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4b6d00e2321458bbbfd00759922ddba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0be14af3a716469e88d3ab0205e19f0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd9c5585a6eb448e95b0e884e456076e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32e907f9d35546f7a1b7999e474f1473",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b255c6c099c46b6adeb86c04857954e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b62c2d967994516b387e720bee9010f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9d4ddf4b39c410893395e0921e49546",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad6321fb983a4ad1933a16ba2bd74071",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bd78baf48744b139eb91f984214abcb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8df4bd9a3bb94ed09c7450bbada35747",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcd1991d49224de08bbe9df64679e0da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eefa0af8b9f0463eafe24f7c912903f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5592875c27874145b401bc4aadff29fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a088fdbd0714452907d41bd78568bbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21d47ff8959e49c6a9a6dca678983efc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd8ec24b32ff4de28cf17ee64a516362",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "155f6fb6fad248a3a78cf63106af22be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1c8fa4b2d334115b4e51720e4a6fc6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4e6ca256fdb483ea97cc3a4a258c359",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "efb02e14da594bc98b0118b14e81120e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c7390edfbf74097b55e885447ee0cfe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5df67923a38f46729f16f286b3b57f5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee76c1f873eb404195c659fb3993e7be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "332f3f8584f741cebdd9a9368e40b891",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5c3252f8c62463d90db482c6a357ec5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f4049f2a5e54d038a40b34db7f2608f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "100ba85f093e4c4d9a38950f3e80563d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e8fa9a515a04a6da82649b4ad3fe85a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88612fbbe0174001998c931138eaa04a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59638040af26425a9a5d0d0e1ddc3fcf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b134261fe3c42d58a5a2922b762c2b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fd180277c154e72941f6dfbb2b82c98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e77cfbae5d9f471caadfd63dc57472c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ac168313f1c4537957780dd63402bc2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb0d552e61534616adc872f162a64b77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6d569664a24411286322addb7aa31da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac839add1f9f4deab0201f967e960834",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "517382b614984c3480a7b886863643e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2346cb7d73342c699b96434062cf778",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68d3c50bc4814aaca7be1c3fb0e7740a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa4e1cd455c6407aa6c2231d52be1027",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e94af5954d2145d2b40f1a42b63ee2ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ca5a50a6c8e495096fa30a5039fba09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec275191ca29482f9c4a4d7f7f08c2b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8573eee7798c45ad87cd4f6d7459b61b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17c2b628682148e2996fe880bd9b46d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19a4b53025ab460faac535c3340bde37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f33a7e53e1dd4a7aae708d657706838d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f8d516f78a44763977b8f83c3e660f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "019140d6ead64812b3f726851f5ef19f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4732045e70934ba2b816b2d7eb6f2d57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "57a5060681b848188ef63594b8d755b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "747f715bb0c74522926d51f4836f5e1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "081f275e092e4451afb0389cfb6a8331",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "939754639d664cd48d52220b2cb5a956",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb392af3ff07484f989d4dcb078921c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "237e86de6ea64c6d9a628bc9e4e0fd65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c01abac0e8249f59e6e33c04614500e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "daf25cac4c8146548f25242b2e3ec907",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d217166630904bd2afc4cd84ef1a7347",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "faf2fdd5b50f4841a123a08461e396aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16aec9f5714a4e4b9d03e16aae857d56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d869f617868b4599a23ebba05650a485",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3500270f42d846a596c8291d06951261",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7dd8e38e4024253a7bd16f6cd7fabea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68fabc1f39944835805fc799999ec0a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9f781a7e7f749409c84fc6be9fed972",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f255d940e8a450487c5f170a6ff9310",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56f0283f45fc4aa6844b66134cf07a0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e58ea30936548de9d304552408f8aa1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac5990eb87a14478a6a008d46b581197",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc0f0c608f9c4741bbade6132cdcecc9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c47e0f71c7f944e5895b7fa03582bd68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3615023cecc14a05a06ae1db1cd84c8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2985fa48b4e432db851c6a5826642c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6cf2c8b4b51462cb236ec3474b22ab2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c260d3603cde4974b836d8bb2ea5983c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a4efdcddecd4270a442efe1380f5360",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed8c6f86b34b494a8f245e2f3b260491",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d193365a742143adaf6dcfd8c394b61d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b00af8db6b19426196988544ec4ba72c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e910edd06c8430cbc51c22b12323f6c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa59ff696a4a4f2f9ce5130c4e402f8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "841bdd068bc1497a82f5c9e7642aa067",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3598a710b64406faecb808d7f2a94f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc4b25a12e744a299f5e659001117a61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9891b1752fa44d11ae2756671bc960c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "879d1ad32aa244de8e797d5a4694c569",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7857094a8b3a4cd4b5b8e48707882abf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6e24c60a3d046f9997b7d836d028b3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b105e60e8b64398902dbf4c59ba3bd1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "573b9ecb1e774cd9801b83e8894d587a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04893e3acc8445db968d11d85ce56e8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a669e1a414b947eaa91ef6469ca01f0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2ad440dbb4b466c93b0d87748f9f17d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86f536391f844fa687687a52a8a094f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7b4fa7673024c40870b184f62ea3029",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bae20062a4014ca9bace1a128815d798",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6679091c05524696bed37cc89fb52506",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c0535404564453da4750c01f1393fd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f0c061ae6c54d84b50f9cf8a8da13a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2135fcb7313349bf83a45f574faa55fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73152e955c8d4969b4fc26eeda5e4fc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd87b21b6d3d4355beeeb68d722dca30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9dad13d10ad84c0c97266f3cf6195018",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8954086ef7a0494e86346addd0dacff8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2370525a5ef148d896ce27d4b264bfd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "729f9f68061a47df8989371fbade151d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "442c164cb41f4185bb4424f966e8c977",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "262f3c9f18994d9faf0df4ff3bf5e7f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adbbc79d219a48b7b0fea3fb736d620d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "936f22a38dd64be891d162ef82726ddb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afde8727fcee47a59b87c2edea1b995b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edfbfbdc17ce44e1990b6c2424699f11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "57f6fa02ac1e41c3a291c08d548745be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c5b60961f3a4903a0aaf1ce8c76fb39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46a897d3d658406cafaa137095ae0721",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "431eaddba7514c4abd2af65c7ace539d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03a20f70d3ed4fd8b08854c63de02d0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "502431786c004c73a8d846d7f6a508bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "600fc3927c8845f9ac9259c77c5149d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a48403c3088f401d9ad5baf968337a0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31057fa6c89e4984bcd9ac405e784888",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a12ffa67b0ee4663a1aa2a84624f8e83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f73faf7dd5b45bc9a71775e3e25810a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3431a2cb85d147ba92e5161db2d7a774",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd10c36b87c047af9865dfbc79dcd05a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60d03091e16e4c0789052cbbf292da68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d9562b2ce854fd1b30b4a47b2f4f92e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1dbb0f8510b431caa4003390d5723d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e0520a0e6714707b72433c1cac9ae57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f80bb5049fdb46f58760704c06090d4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81d5f4e8c24c42c88bc38e86b5a843e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1350ce7d9f0c4d868b252e383cef3ca6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b870f7891524ec98177d321766318a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e6e6c4b30334f3696613763074955cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfe3de506917451197722168fda00497",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "936bebd4f1544438886d7bce962c63ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c406f32624ae45b6bc74126717eb1b04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7c26902c21f4db08f1f988c0aa9d34d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79b1ba83b1e540e9b6421af11748eebe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbca53974e054ae0a210235982dbeacd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25e1d837690841ab9acfff1dae594615",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "498c429a9ede493187009fa120ed7ec0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f95161db77064000af40433afa3a1ab0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9b7ef733a734fc2b1d7972d9f9468dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "efdf3ad28dc24e44af085ea7aa7be6d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7b96914d7fc4462a4923958399943d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8bcd5e9a8e04d3b87883164db6871b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2f3480c7bd345b2a03789b269912c18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d14f66462dd84f9786a10e68ef4ab3dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "046898794ce544ad8ce2cea5859195a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cde93f1f254a4ac99a37e7ddc8415a80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b353f7b3d5674794af0524525ff8d318",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61e6c24c4f8d4ae0896c3763db0aa529",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73c7e5a32de44068b8b9f49959d96757",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8afd8570bc774cd3be280102102a859e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1925530c4b084a3ba7ed4c3b2b5d01b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c8b8ac25e7b49bb9ed4cb1e5f66987e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4bacdf2461c94070aed15502f39a8cfe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "576d8464e32d4bcca7d9d558e1640723",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42a71221312c46009a103931f131f1dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a42f1cc8d0e4a738932f9e70db85e36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a008ee75b654a779396769d91dce661",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "234904f99f5a4350ad1e4859edac0892",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6b2163ac47f4083a330740d0f72040c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "119c312c077e4ea28dfe7884b7cae3d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ea74b089284469fa7f192181d692a66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19b3fca822774ebfba83460f72a599c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ecfeb16672d4b2791678341eb16ddbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83cad0f3136f4f29bad9fde31ec9dddd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89aafc6daae24c6c92455e4a32725607",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2aeeb12d045a45849674914d35185e21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c49f4b33aff44be9367caaa1b68f35e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5ad194befee445eae9c145bd5dfc4a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f09bd8f9d40419d9d9ffc775120e3b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a4a10cc29324ed6ae5dd62e7ffcdba7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c47dff3efc745bbbedf9e02b7094702",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c36fec6ba534198ad53558cad531b31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30dafd24bae244329e63d87bc0d8ff06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e11dd1750d64c83b2649526b869d740",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0b82e6793624252a206bf9873d1c7a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a37d301ad69148bc917f367c9dd00327",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3aa98c858ed94a2196d3cd98de5e93f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb618bae1fed4ab790ed8b3fbaffeaf9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41da3ac9f7754339acaef57bf2d534af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f86b227912a4ae7a1d8c11eef38eae0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30ef613f7c034d5498e1f52f5a91880a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30b9656aaaf6435593dffb3d99f4f43a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d91b5b95f2304102847be3c9688506f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9991387437ad4a03947219d07e622fa2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e5babdbdd914dd9bda2a2c18fe6f312",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee7e77bd8e8d4356949c619503010953",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abe9a19cc482452fb668eb0d35bdbbff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31b77dbd516d4269a791f0dad03824fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15da6241ede84532858a35fa30e28b6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f7f465d7db140ebaf66d8442f73a408",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e97d253091364078a5c376b832d44449",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e937cf350a84da0bbe327f4d3aa63af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5958a28b10e54d11b1f4ed6050d96368",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "464d94e819ac497c9638172bdaa6f18c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f43868e292624481afcd3dc63f39eff8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b266e319da64f4480f666015625c1d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c88eb6acc2f489583daa84ee01001e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eece28ff236546dcbfe83f4260b535eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03ad7bed1dd544b199a850ac947c711f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84543a07650344e19ce4ab2c9deaab40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2a4c5c40adc4c9ca86f6af66e85a7e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c93be5cb7be4475a8ebdd79ecd5d46cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2309689533e4cd1a928b6b8401b2fcd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "624004331b3a48b49ecf7b1d8aa7999e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bf6136fbc064534b29f60ad6cde4e7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f607e55ec2f4ffbac4e127e93f33405",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5fdae6bf6c834f858e2d473f15517734",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "849c56f7938f4a17be13ac6cecbc108a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95e342c040844542b73c267e1ad21c4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8da3afd2b834e548a0959ac24aec625",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2eb04ca0a3314fc2a11bd65ccd88e592",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36331c746d1b44c0adc6ceab615c3727",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf54931a1ffd4be788d079d40ac77b55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c347e0965230462482985f273553b5f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1863fa2ab8bf474d92e6db6f552b328a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53a73f14615a4b8ab6042686557aa292",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07c9852fd34840968cae97f2c16381bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fecb5a0fcfff44ed88cc8589895a77d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0dfa8fb931f24a18972bfbd099ad46b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62517ff24a20479f95733778bf8c9d65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61257a1841aa4619ade3891ec28cf4b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05a697b7c666439f97fa3b652a1de559",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35d16903427e4775b59631a1011c2336",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e4b070a31434510952627fbecd49f19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fba954740f484f3abb37ee09b33a716a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0435596a15644badac050f97b2c39cec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abc3ad7d017c41e0adb5f07436bde669",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83626fca2a6a40f8b8320483111c5018",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1682505f60b84da8bdec0453232a57d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72be4152f0d449efb7f82480912b6c52",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d3aae552a8e432ba593baf2c3187bb7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "622ce4a3e6ab411897e8188678bed6c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "255b6590ecf84d2da09d3dd5b0a3cbef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb533e6cb58e4809a94124bfaa4aab68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3acbb4f252f24a4eab4582102f37d77e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3f2e56be23b44e3b2497668fdbe7566",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fbe9c8d51f74452bc8f1b4598989cb8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1955118b68654cbfae84e29a32717b47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68a386614a094db5a7235c6bb0508b82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fd88c5679344df8b0ac4df03a4576d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d85e5ac2d6414d849499188957e280ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74e0be7c0cf045f08abbae31eafdcb1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38b74eea46ab4713911a08b5f90bd45e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cc9fccb4f594581a19eed1ee3a94a53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f20390c4fd4484f8da007bd51dab66a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7cab42f03ac4ed28467404887f04267",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9899d1c9f5ac4f089f65d58fe8e2ef08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24c2ca522ae94d8fae24089afd8771a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60fbca8e61194b19bf8eb50bbaf77d51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e97378220c744a8baa50b3fbda38a2f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bcc444979ef54d508718d63d691596e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "185657239e394ecbb9bd308be3a2fa2f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a0741d159794a9697851cb998ab4671",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a511f9b5da7f4ed4901a4762d44d0de9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69f7424d4c9649f18a88a52f68cab773",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf471163b7bb4b0db0f308770d43c87c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c53ac4a6f8749d383eab3ed5bd975d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0e996d2029845108183ef52bd79d109",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fae13d6097c240a2989565fc71a07ea6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eaed8fcc75b24b9f844580b4ad7ad612",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4deba12f1964ea0b2e6cd06cf2ea5ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbcf3563109a4f1b817bbdf99da8152b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bae30da4ae524699ab359ac753ecfdb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25dc68e9aef541f4a22ef9680eceac94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d749f342d361453c984fe74fb9d0df8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83efaa873f9c4e7da3e28a61769ad0db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bed4dfd455c14bff84226add8da5af34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0e791be742f4996b33e28ebc06b9026",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee3c1d2e29814217a74f6923560855da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "212c428c265a466793700c540ffd6d38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "562f24be684844d2b5298674faf93770",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51dfce5717ba45f78095fe4ec8adc4e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b339eb81c154c348b732d8fde33e810",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "976b711865ca4b3ea34be9bf695b97d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77e0c11f86be47fb81f5bc57da269bd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6533db7a94914a4399bd125973b00829",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f1f598a60d343689b3e123666f418eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0934c2a40a9e41488db2caa202236c4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "615f2fef09034afbbe259a97012343be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27eb267ade944e70b17da109af90a59d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a90378cba124ea29631a4cf954984d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af1980939bd148e291a91b28c744312d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b92b445f94974d00a42ec7a95da32b65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b84ed8e4b044b5db9ce302ecf23fdb6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e585c2a702e436c9035b24116b0d47f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfc099b3d3a5425eb8329aa401001aa0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4f5d8c1f57a4a979d5a83babb59d54c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66b27870d8884e05848df61910de45e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "104734bb7a8141da8afd0dd3dde327b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90021403d2a442be9af376a3242f895d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c58745a55b064547a7ed0cdb4bb49651",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98c686084ab048b58f94d069f91eac4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48246db695514cc5a94e82603c715ea8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47ce3357b0894c49b291a0cf7569c1c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8d0ca750646458fbc8711fd221560bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c81f7416bb1e4b008fafc0dd84b7230f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d59000fcf9664a0eb7d296b0c94a3057",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c87e32a0ba640a698fd038f75828e76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75cf0283b42e42699f3a02226ffa41ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "899a6e256b7e43859accc067d670bf75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a8305a9e7d14f82b8b27a627d9f642f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0e9b62d81f7482984fa728907ad47bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ec51e14df0a4fbbb1a2d8fac6a20ece",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51124700a4904b17bd0a2681fdb95ea6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2446d9f730b24c7dbaf40a0fd35c363a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35bb24fc43d64a8dacc5c8b26a15ea81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d613813a1dbf44e0ae58dfd3629e889b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5119bb43214403babfe491651ebfa78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0da736aff71749afa760416379b3e4df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4db8e32833ec43399e9d9c036783bc2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26ecea04fa0646a385b6ae0095b84ce2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e74166f6d5574b83a57c7122be14f4d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87dbbcf7304748c490a481826511c1c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df89fbcb70654f97bc0187df158e5ff9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41e82ac2b20f4fe0963f514278a98737",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "483a4ba84eea433b89d535a09c97ce77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a00f7ea479254e0da4e0dd2ad2da3f82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c16e4f7c2a45445497f63eb40a317d0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bf9f5d13a7045888e3a5a258689fc66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2dca94b4894148efbe261fcf0de4370b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6673eccc867464a99180a10ccfb650b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "243e3b5b4998430bb6c074004e94246d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0880da3275b4832b2e33c5e11781241",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "302146636bcc40918525e309d1b35430",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e0a83c89f5943dbb581eb6a4b78914e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e8ad70f1b794875bc762d105ea34b67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1366cd64645a4a0e9d7e5aa15c493b11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db7edf862a424e37a9f4b0aed5f4bcf1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "534831aa855b4eabaaf981afa2af089e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "091e2234980a499f8bdbb401b1da906c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68b6cf29496547be9035f509a6820618",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7cc25b6289348aa9c0bf62c615a3a5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ac585e40fcd41af8845470f4d917497",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55645182235542c093752dafdcefa7da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd0108bc043d4b0b9f61edee113d1d0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22dd53a2d7824904b8886e3312531468",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a41bbea5bdc442a38baf5cbf4b241975",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4338fe440a094b6c833fc57bb2e4bd01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5d29897d1fc4daa8bc7ec09ebd9afd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e32cc82589646a6ad4234d5d6d6153f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8920e53c9da49f09aa25ece14366be0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e18a977a7d4414baf338f30608580c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aec0e213b9134152a22ab0de487a381b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cca50ea108ee45dd8b6dd76dfec78b5f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72a6e42bead74a59b0759c3554908e1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10be5d6a5a674a77bbb5049eaef34bb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db15b5797fd84e8fadeee43a98817586",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "baaa754f53884818ba77d50808fce217",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e5e2937c59f41229295c36433b04db3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fee46acc695847e8a53d676f0bf38497",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ffb123153bd4e5db1a73437cf871d74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5ef1f09d7dd4b1da24f5c04d393e271",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a12259c8f034829b76fd28343660bea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc16aff5687744a99e74c091b80cc09d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a10b1b71384d497cb3a1b61bf541ded2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bfa13a82394465fa5afdd0ae688722f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40b121ba2bcd4381b0b9e2218b633125",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca284ee2f0e3409f9e77ee3048a0d669",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ec9e61648f948f5bf3fe23ffff4807c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62ef0aff775d45bda45b787d08535160",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e17af81ebbd6415a95e9a70b4c475168",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "131e008162c24aedb8985079e7768a23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f5715ee83eb41b4b7c5071dfbd16587",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "697461645aef4b1d8bdd5c92b5598b35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c84125fae7e6471cacb7e91715fecf96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43739dd3a9264e6ba9356cdd57a71d98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7595547354994aa8bebeec1186bdf4bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f54313a977534b48a5bb95413345c599",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "259e2f78e012473891dbe9fc6ece5c55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "489ea0e02809498c82505177a0417c11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f730a3d8fd643218d0c494cfb2dc964",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8abd0858dd134568af5ef3edbb51c881",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37c94d1d54714e27be26efd43c26fb95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6382ec6de5f4d72ba04ad93e87251c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "907ad409a38e4004aa0a56e41584efeb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f501ff9b9ec4ed5a1fc37ca01dc4bca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "323c704e87b34513bb23a21d024d1fa3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "415131a04d86469da4ac0be1286b8a4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7acceccbc71a419286926bdcd987c9b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9423a0403f344108b8455e48d852c17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a6be0d23ef841e89e0feb6870284f72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e0259c07a9f433e99350cb80af772b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2bdd783182694398bf810d5e21355dc0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04b56c6bb0dc45b0b042a53bd9a10f29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d286913ec7f946e8a131640d42a16ae3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a30ccb3c69454e3b92c3207d011b2d27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fd66c03ce474c6c9f42f9b8ca93083c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bceeb592c9be4d0dba64cfd439bda8a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88b8b415d6a54039be0f80d3a73dde22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f04011a0bc24ea9a951bcf64f563b90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ed158372be64d6999ea5b95ca24af63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9237ccf4c2d4be187fd5d47a33e139a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5695a07bd4414b92a82e8ff1c1a53951",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff33e7606d09495ba52062268313890e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e058ffba90f459989cd01328449f772",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3783604c4ba64799ba019c25d1bce29d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f010cc874d924f2f9998b7ca4a875619",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ead03507c683428db8ee67865c78f3d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ad09c5417394877a19c46cca115f9a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "977d5a9bd3a74bb1912cac63720cd545",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef9c3e077d644aa4a10f719be741487f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "efc88359628b4ca68840d5c375ac4ab8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d0dcfa1ea024e91b7c050f807193854",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d45c2d09cf034e62a8facda2e034b320",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b736f7fe39e4b0d9b21f5a3c3f73c3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4036dac32424ca29f1e338352549fb6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2953ac1510a74ed8922b5b8254127ced",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3d65e894ee84540a5755bd3a813688b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3372c2ac4cc413898e75b2953ed965b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18dae048044442a480cf8785e72a63d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39e04737dc874e8dab4eb2940f8eda2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b223d49856d542b99cd2d583721ac942",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e55ad5508e314c92b4917aff39dd66f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f8cd78ef4524555b27891877083769a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bcf7d6844c645b685ef197c044edb85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bafe51d0b7934935a2f9166cf22c24e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4ac51f42e4d4ea9907c3bdedd78e5a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b18ae5cf21b147d48a023ce66d71982b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "590b05ddf44f4879a9b108e54725ffd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c0f9ec236694676a91ee7ba97f4e1ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc56e9e06e6e4a23a465ef53eb2b9682",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae3afaa1e27145e19c00ebbeceec3d5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bafbeb150b54196a39ff33095dc35e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fdc057f86f64a938765c868c1322905",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "948a29c10e3149019a00f71554322d87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "460f762dd8464ab09b3dc3a8b83d3e1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10bc3245401142fcb34a493de502328a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8196172355ce46afa6ef950e60c782a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ec92f675d6e49e1bc3fe7dad33c089d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73e65e5180ed427a9cd22358ffa23798",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29ea12e97c474534b9caa25e234a85f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe14a9e2237a4512a578bf14584dd228",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e99e06d6133476e910c2abf5f77fae9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f9f9a89d49a4196a6f5b58caed6ab1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5bc5c4503cd45d59809ed03ece05800",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94de1921915a4125a163b06379f02a32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41e65f82bac24ac6a65c45d59e66b7c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d5d0553056f43e0ae2a081c3bbb384d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ac99bc59f6c447ab6e59e9ed7e87ab3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "584d9c5fc3d741239f4bd9da67962b3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f1bf7f38e2644629daf4dc8a87ed036",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cc8e90e7dfb48dfa0e981f11df1d9b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec709fe01a814f27838c99cce40513a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d27ba811b83422c97879687b4c04861",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60d8e3b00e5e4071b588d228820bb03c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10fbe9095d834418b95257d8aa05268e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ef12d11422c4a86b96e28f34099dfc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1cc5ce8a6714ab4808f805900456475",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3241376c1ddb43a5b2db8e68c53c558b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edfb2ba66aa644f3a0ddefe6b1f81015",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bc752b9708c4b79bb87681244430e41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9310b80e73b4cfdb565cefd5ee7bcd1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a692ef2dbb048f0b0772b76ce69aaea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd700860d51c458584861b1d9847c586",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5177977415445c38775812be6f01ae8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64aabcbee8ed4072af89a16139b22fb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef83600d06ae4a1e88950a7d1985e13e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18c87ebc09b54774a75d597dc6c1a895",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d06db8ae1bb34121bc4498bbcd219589",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38dc2a0a2e7e4911abcea4fdd4c150a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4c6b304720f48758adc105f24da97b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "397613507c114c02882f844bb9dd7878",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffb6cb93e3f44c0187c2882412ca1a82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d0e8deacc6c4dd294916bfefd6fdfb6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e71bffae10ea46f58abb333d3462411f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6926ea5ba5d54727be201fd37b642dc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c50a530e4934592a8f9a8e891d63c76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cde3e8b737f24a4492d6f6e1b4a843ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80f02a31baed464ea69ed71e83c89eb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75c20cbf09b4430b8639098059f48b2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec60346588f94f5899ba45bd3fb59e84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22e25dad64a941f9887304acde72c391",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "781796e45fea4a90883ff8fbcc88e259",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe4ea2e0430747a6b75193b72e8eac2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45501b43c1ff4b3a9dd1553964a9b8f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03a41cd18dc54b7d999ad07c514c8f6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25d2761303d2499b931722c99e8cdf9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f726a340846424e972e704df16c32e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e13b196c4364563bd390dd83b54289d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebdcd5c9d4084291a8963a1d3b5cbe54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6bbedefc3294cde87156c49d7c0495e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68a7747e6df74d47a4093941916d0f34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb1c95caa20a483d8a46d27b668f6137",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "222ed823b8224edcbefe5ec5be106fdf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9110efe48c24aef9c2e4f0023e9a403",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d4902680a7f46aaa5311ae2d9d1079c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0c65e82ae444f97be1ed48fd1f3f679",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fee48e8b61340d0a0d74a593e0b5f5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34339c14908242769134bb3a50f47530",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c1dc3285a03441098f2969f15e47a2d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2f4e60f84d64804a3c83e7ffd2213ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b366697b75ee467787178171cf8b1194",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20d8050e732448579cb3957900f67311",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2191aa03ca2439f8a6a4dda3bf58c3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f6de749f97545e2ab600061129ed6ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58421ab145e6450bb65fe5b049b64add",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20b0ba33012d47fb920422efcf7e50b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6635a8bd474441a5a78bcfb2d8bc756c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42042e8a297c4046a48012cd2af4b737",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e1c4c21dbbb4199b572530ac1cab517",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2a1b6d8e0f645e48f26b5d9643f2e6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d7a81a9b4dc4139b744674ba394d971",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ceb7eee4ae06405ca4dde66aa63d73f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f22bea9c53204dba90d4620539a1c2ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e82ee3ba68aa4dd8ab8017a2456ac46f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b028aa23c29431faf44fad9924d7fd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14db2e9dea9e455eab21c13db4562cbf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea7bf9f8172c45dc84e1282a684c9658",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1b5141a7ca34f6e94550f8266ffb142",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8b71ae7480d48c58ef2d19b0ccbd911",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "168076a0ad964f129d6c9a6fe0289c7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a567a2c7a65a4b1795dadbcb69bf08b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef6b156a8cc448229c2e8cfd0b11cab5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f333ee4ffc6e4253bda0c3ee67871252",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d09cd1724d7e41eb876a708a02177539",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13b6c98ec7db481f8ffaff3e27fc88f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8507612e64a4223b2cdf3a125bfbb57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3ab5b2bf6874332b5b3c38ec16a27c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6cef83e757a49f9855db5ab8d17d5fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "807495391add470d83db941b3e4653e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62daa4ecc7b940d6a5543ac044630b5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db34ec0b10ac4ac1959fc7f7bc223f0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19c294dcfbaf4ceea1ea094a764ce0e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e121dcad96f459b974c529696cf751a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28560d4d86b54a35bb5a3a2aaf081e54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba22650630884f918a617604dd919c67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "185eac9758d8496c8895da3f473907c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77cccb5afb714887a0928d11ac57f733",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5c8c97ae2a44810969e0c3525f473fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "893616bfeab14c96abf2a3cd28d01d1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de503415a5c048eab91d082bdd145217",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb04d4532c364496835dc24703e9d645",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1de09ae125e40c0bbe6c4c4be5c0c82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94b1d559a4d54b9bbf29669324110fba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fcc725d740a4b1ea6551d222b8e51d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ae11a8b51f44a7081d4b09188c8e42a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07d37cb080794c29a8aca8904838c08a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f2794c5b9854bec926b7414b03db8ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbd9bb63b8944924956b496d709db7a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eab682df32d443b98ab90aea538d5443",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dac9a98100d846e58c9480411923d9d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acad54c698a041119701739a92a74adf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d43ba34a1eaa4913aeb7fdce6d68be65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a76ca698e6c34334a314ae59e4d8432b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a6bd3286ac14d828f5a20eb73138f45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27c63b7d4aa54acc811e19fd273e6488",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbdb167d321f402181ac5619f527dbad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05f7fe60ea3847249649ab907aa0b098",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bf441d5e4864ee887fa02f39adbed15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fb9d082b4d74702b6549b051742a3e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e431b1bd49be4e248adb79100e2bafe8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f89325db165b42fc88e59cceace73bc0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7071d95ef4043338756137b7d006652",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "487390f571234d2c93857f6d1f11f458",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78e4c97cefe8402299a7d38673286bdd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31e41000aac44f8d957348b209d2e0d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47782d237b5e4ef6a7012ccebc5929bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edb273817d2f4687bd6ac3b0f9d8bf0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7230ffe0e22d4ddaa39397e51ccc9d20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2e1fd9422494229b498aa390c25bbbb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "770d19f3e7c34e9ba8058984dce43555",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90cd13ac119c4c34909325c52e985ed5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f411eceb06f4fb8bbd5c35bfe537e5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe3cbef4714b4ec98f8a469cc806af91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ad2459d42674ed59266208ca9328145",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f22f05b4de80406b8364d77c61c17b19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37b186eb911f4e98b108c852c96faa0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "698a876ad03f4ccebfb54ed97c23483c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75b6d83b724d4789a66d9f0d6f8ed27d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e35f1b5ef9e848cb8ba2018ee0fdf302",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b380016350d14558a7c3048c6ed41984",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdd93dc5a97c4288be26aa8643a15aeb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a0095c2e5d940579bb8c856944bfc83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2b90b9f3be04e00b89b213910c3f8d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a53d27f66b23474d9a7ffdee2eb1c33d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e56bd77c3a54d919d1ae099c22e4cae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b8bcbba2e234e559e6681458c8bd915",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4abab2cb6c0485389f595f6c882c103",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c9eccc248264abf871c66fd6ae270bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ccd6acbf6a65485189fd763dd0c56b72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2c96f5aea2b4340a2df0416cb730119",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7b75b551201461e948f79c8d11a696f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e727198001d43d79b859b35e9195897",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e483185e8e864548899c58121063bf3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00cacaf80000449697a37762007ad4ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df42343b2c384e2da4ff26268040a3af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "090417dd397b4cff8997ccbc85a46618",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84fe86371c1a4aba910110400918a002",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a813a1ce8f4488bb05e754ef8feffca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b76c972ffbeb4acabfff1f478a5bca78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48b08f5060d34c0a9410926d1b075999",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c854ea6149cb4aa6b8e0fc734c9484d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61600f1388e94047b02e7916222f2aa0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c80b9ae43a642f3ac4b8e48f9541220",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db23129fe731424fa93f7c4ce6091042",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f132535db9574e1e91233dfebfa0211f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ab5bc5a8e2a4256b4a2a873d6711d9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "622feb469af74c80be4a5793256cb5c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c437b0c3ef7d42fea692c03b32e4ddbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2b2032f91464047948bac58f2c3b811",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7615775a7414ddba0b60e8a4767d975",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf1f816d978c4793b0de4100ee4e740e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4878fed91e94c4aa96dc1bb6f4c77cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68062c9e38404df584c230e9348e5432",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1fb608249654d6a99f3d801efb49722",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a55471bc27a74bcbb0978f3cb48574db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "752a98f45fef4d10b7a1e8a9ce68a5d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "300c26a42c8c463d87c29a2000af6a23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36b8c5fce62443fda3634b8ff2098e35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b1542ea8305441e9f5b8eee15188ff3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5aa0ec1fd2af465ab2c0c57b5ec9c2e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85cfc38ca05748c2bc6b3a63a9b1ae4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5edf05a82b314b8a9a74006494f0b3cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18d82c2f86364bf4b62a7844da894430",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eec6e703c5c748709be3e6b8f63278b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f956d8a8fa664c9ebfa9604a3824dc8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "950fb664f7614b14a9777a9b49acf5da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00ef77b9e6fe4960be87ce30303d76cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ada821fa638a4d768b49da4ff4099123",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36fe29ec21804ea09700ee55eec247a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09772a7c4b19415bb31b9b5846bf3b3a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d36935b38464fb9a38296dc996f5e66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "392c00fecc2c4016a07cfee4b8a9fc7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d589014c4b50429ba377d4ca9cbb8613",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9fcc04281174bfea100c679e8e0174c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51680e1fd1ce49b8968a727cf506e6a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87af2429d1084709b388d39b5af0271f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc590568380a476a80f3d5dd67a1c2b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4813718e2a4c4cb4a0a5042626a38f28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9472a18870f4b0bb83645d69efad1dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c211134ae3e54f4ca2cf5a16e02bbe27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea7b7765b11543e99ffe27ada7936c10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e192be1ca78446e491f7000aca7eada6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acaa7071c93244488ab275c764a3140c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1b112b8410c4a5dab907b7b6e9f621b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b2e9331cdb1463ea022381c37d2ea05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1ccf8387b1e4fa6b9fae12f047634de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34f9cb38cfb54495b12e0bf55ed31bd1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1c37f0553144a48b17c3c19433a204d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afb04fbb53924602a3b5d44c3d83b12d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6cdfb3c3a014a73b20c053689b403a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3baaef7b60364ae5ae457c7e55d9a2ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cb642e7332842179b2a539cc2f4f4c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17e3f672d9d14565ab96ef8e7e976912",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e05f6b4f28d4d68b7280d5daf3fda82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a216514707044c9eaa1abe59d0fef7ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a714ef1615642f987e720d5593d7d75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "579ec2c795304bb7a96aa4e8fcb22948",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff78c5a3784547979fd08c9d5f62cb85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c5afe068cd0482f8e9e8ed9d78c9eaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0307982f2e847beb75f3a89b24e58d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33e676ab4bcc497ebc43d1941d726253",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a9128f5c6744fbcaa6ce2dd7697c3f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f7680bd72f345d3afee6f41f4d72f07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82ad40a7aee542bb9c7880604540e0b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d2f8c3948cb479ba589e8cc6e63ad66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "501c006e132b4bb68687d33adcc20eb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ea742144edf4fd08f54d2a2c8c30912",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f7c5e4607884036ae6770aea9b08a84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e255d967b814fb8b867fc06a4fb0860",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9710a2e31683444a962223c6538cf9f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd836c20d13947bba0c50c2ac7561078",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9f58ac578544bd9acf48ef06618b8f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14ecdbed488b4a15a752026bcdeb56ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d4a19682681415a832d966e8920c405",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3bba081c4524016a07064f3b9aeb710",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a60823f7a7514329809d2a406b5793b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "646c342d3eca43389686b96480f3c979",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c3ba92ac07246348e50e29c0934f3f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60c3dacf63e14e299bf96e2bb166afca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5ef4d73a5b244c2bb77545afa344df3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a3962928d99485dad716eb02a87f512",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c23d21c069e4a9e8bba70ebc0a35b2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "972bd6261e8540c9a1589723b881d609",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46c8082e52eb4588b3f8f2bb8092eb76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8007010ce2c940abbce9294ba0f39007",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9f15b0d87204c43951c9ec6cab05242",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4907659802e4325acb526dab865853d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "702afb10ecb348d29594af6b49fa5f3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "659a2927d342495a80f7353e8068b3da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "421315d9760641e5bcfc708718c3e70b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "046b0a16848443f189bbe8f5670848f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66554150d8e24476afd528aa3099f496",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6200c0068ed44d9b1af2dc815661be4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3b403ba93274b9589230225739a5fff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebae73ceaf494fbe987ef2d60331541f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9d5e98338ce4df090923e011495c0d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "334b61f476a245ea94a8cc3b74ab77f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5b2adc83c6f45d785ded33f1d7fc589",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afdd4c6c8b1246999d2f25577e74042b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de22c5a0e6c64c25be479fa2d757713d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8109a56f1deb43b091f553c654520633",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e71da0d542924f5a9b81016ce177e56d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2817aa45cc994f0d889bbe50092f2be6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9dc46dd855aa45ea8568ce4873f116fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0736fc954954ac5ae2c8456f96dc041",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c2b597dc0554fd6a43fba2bd5bfc4d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "480e48334b3946bf8861d4bdf1596355",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b894df880c3b4ba6ae90bd5da7be5d58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "472c5130de8e41c195aaed16e0ccceaa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d7471a4326a48a1af1f9093aca117d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc5400e3bfed4257875c3d062905012c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bf543d993764823a826c7c84c9e1176",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49bcdc3afa784cd6ba4df083822091d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b70b4348fab46948cfa18e781d47f7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20411a3a4acd4025834578ec1dd8a2b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0225af97ca54433b91332e936faf6833",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a726fb2d6bd4ff1af365d1bf583abc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf61c0440a494edea8c984f7af19c817",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdea00aff8d44402abed65f7fd13bedd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3345ba0768ee4272869c256fb6adccfd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8513b98501548e086ac3a548dce4b8c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "524cd4774e3c499486e565446cbfd52f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f96f2f05862481e8a41239453565c43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f48bd3c4de14349b6bc2db8fc1407ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24358efb295640bca90cc1b61cc659b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55769dc2eea6464184db27f446f8412c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa38b4ba74b94329a58a296add376ac5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19f37af5f0844c06b6020d97719116ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6365f5e084e4a6cbc33ff60ecd793d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce3b6f0b7b444cc7878481e80139e2ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f6711a3986842ea93d7eb92013d1bf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "930a3df47f734214b6d6ba8722b09552",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23dab27905c6487fb1f85f5103ce36fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "946ec3d44a3a4a42a03f22f6e0699906",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "284ab636840a4e81ae8644811aa8a0a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "daf5f89be0fb4d79af88de416ae986c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f630ee77e22746c39d7ba66b9244ae7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fb11ffc0699482dab88c5bfeb2d9f2f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df715a48c0104941aba5fcddb08b8c94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7227145caba408a9fdb8f0c549bae6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2240733d5daa4b9781092eeb19f619c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cc98da83a6c4aeea931f2fd8efbf4d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca6e916871af4707af2928605247bf03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07d687a4af524bb8869c602ecdd261c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2302ae9db26405ba0b50b670d3e225f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7b22a6f0f814e0f838316654227c247",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b24665d8a30542eab2e3f6d748a20664",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82aa970f831d4135b55a3efff08890ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a1126f3704d46f7b83275ca20589779",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc3eee15f04c4bf3af932ba0da313731",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "183649c253974856bcec6cd3a871a5ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a37b8c6197a40a0864aac24ffca8f5f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "393dac6556964edabcf32e0dfd400a27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "370e97edc092449a805963079cd827d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "044eef9c7aec445f8863658d1adc6834",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c91a77f6b644a638dfbc5629ab9d39f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "880764d302a24da688b4207aecf36775",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef1616d95c0b4805b2aef0ce06675410",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd7b5609898c4b01b1fc01c36bbffe8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f15b3ce35b144624b8a2d02d96894f6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "207de6ed4a9a408e852d722996abe61d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01dcd5aba95a4d6dab5929dd0869505f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9b71eca4828494eb05b87d154db5af8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0812c4333ed64278a65f8e4f67687a0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2affc6a9c6c4db3819e93b3d43da651",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "346e3d5ee6964043aff45fd83fe17ae7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c72f7868e50444e9d08ae8dd45af2f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c670991f3f5c478fbdba828ec6c378ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d49405ab1e142ba8e1ad1dc7ac1713e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33e9afd4dd374a7c9a0a85c6ea7679fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e1c277b9cd946159c01696dcd92c62b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61c0fe0a5fae4af7882943ecd8a2ef01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0dd7e16ed524050941ef82ea0e336f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84a2fb661ba249428227197bda595380",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf92c9d6865c4d15a14845b41050ea26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66374fe8e655473cb4596018682901be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5bc8648b4474aff9267897d7585c1c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95de54b07d0d4119b266d8580440b7cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdc66f09f6704dad95831eccdc6298db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c17d7db2aaa4351a668e32da2ca2e6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e60abbf978c46fb93c376d72ba0d688",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fff0657e6c0d42ef99aaa7fc7c58bf6c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1d7324a8cee4ff991780d022ba168e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f7e48a183384da2ac2f4f4e33e60780",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c173c8c3c8b4256b5cfbfc79a08df6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7992f5e7b42941de8770edffbf65e688",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c8e52c668b84a6d9df471bd7b0a0bad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b20dbde4fde410391c75c0281fae5e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b19ad7d42634d6e809dc14c38c72060",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c99494d8c09b4d00978f9e77756cfd96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e37ddd12005441f78c9171d3bd7d1632",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f801a8e2240144d287f9a610b13c5fe9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8114e49baf354fa1a0aed22ede0d4ed3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a58d79e3133466fa1894f6e69d58ba7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "209d986b25414873aecf1718240c5bbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0283a891f16846eea0035c8050423118",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5680f36f3c9345c291126ec407ca32e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a8f080113844ee8ae80f5980c8edc50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3df4cca35f9f4da09ac1d91399d0152f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4165b82db9c84399835633c6b8f2eac3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aaf10fd2af1442b28e2ed2f2046cc563",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "524d9f63a7994aa2a39a7628cea29cfe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62caf91a94bc4e5a95d9b3441452e3aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4961c5c57f98425fbfd1fa94d8528f9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ff4e18bb5c04eff93f08b3da1380c27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e41f918c31a047a9885edd336a731228",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff82ddbeeef94cb6a39825b3017be692",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2a74306686143688ec074ac0f04fa1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c087ced9725c41cdadd3da542d139e26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bce85056b3a54ee3a453a671b3638ac3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d43949a417ee4fd19b056bb56652c7bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84a98626d7e84d93a4d516b22e5ec9f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5eaaa9c2c0f844e4ae31f8a7f0792eef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47087010c69740c8a14503cd673bde46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c0e89f5117c45358b1ab9ef6327f1fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eaece8ddb0b847959e378aad0bc87774",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bd85a3670fb4368bb175c3a0e78543e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d19abd8da21b48c38ad00ec927e63887",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "222d774d94a24719820b2af11fbdaed5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "919ed3f4814e4dffa1fa56cc198d67ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03df3969ce4c4a6eac9a8361e0efe141",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5354f1d623a4882aef0729afc2d9be3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "294717ffe85843d1a5e86487e1bb4d43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8309de6109ec45aa9d0f717fb88b4bc8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c3b6f171f014df38520fb7e2ccd1ae6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17f194164e0742a5aabc5bcef9fb2549",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05757849e05843ce906e5f21e66232ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "beb5bebb257d40a28280b784f653572f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eaf0fa8582574c78b838175afdb4881e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0479b549a8d496f87f8b957155c50fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "012dd201537847f581ca0f732a791f46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aba599860fe1488690b690c2cb26beb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74d097f95c814a538a533ae7ad1488af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "676a68acea5a4b7792c30ad6d9b6aa92",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16e8da0d3beb4a0eb77cbe3bf852d36f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86d66ce9f01b46408dab7fc01841b882",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ccb40b09b14a4f958de6b4ee29d91273",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5485dc811994ed9ac71085dbf9b256a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8d8cc0953bd4ea0bb681238bb18f97c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fabdef21e94446308bed66bf07e6e460",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c65eb3a60e546699b114fa7ac5285e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ea7a54a571f4237b4bc0ccf6ceadaa6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37de05daea53427083d502593bbf7e23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cca1d8a012145fd96c140d79e96a56b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "639868cb043040a7b6f9697262b99f12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "676a11e0c6024e9eb53428d6d68aac81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04d46b74a3f34fb6893a05006cd857c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "773482b67003493e9c6a024c0657269a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d6491b853c1436ba0a5bd6fe0d32070",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e45badfe2ec443778643cf1ee07d1c88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4270a6e0493941eb9d91fb1c11568392",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae11178261e04bd5b27d1be6a08ea4e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3422b2abb0ee4787bdc3482bf7bbba48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8315c3665b5a427fb3601e4a2824654d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d8beb71c26b467ea3a4c7f88b22d6d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de3263f4eeec4ac7bfcd233f1be4f94e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34b9db61596e40fb872dfa1336314f43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd3a07b39e114f689372a7f3c06c0b0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fe7d3f681ce4dd3b37fbe2164f76aa0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8aedf15345ef4522ac4f409a0bc6157e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1aa3a52e81cf426fa3052ece150c893f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58c7bad0173e4a1dbd2b7fb92a07f171",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80dbcf9a93bc415dad1de914ab160d1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f4edb40f42145c68b1dd898252d29c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30a08692c05d4544831ba86a1596140c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ad765df20e34324b4e95b0a914fb1a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03662b8d7ace4283a32943e17f0cb5f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c757b9e028e4408e9d752cf59899af84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64244f160a194b488c8a112efde1c30b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ede0d67e168640dfb1cd38d79bd2d571",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "name": "stderr",
@@ -214,13 +14228,6 @@
      "text": [
       "`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 999: 100%|██████████| 1/1 [00:00<00:00,  9.53it/s, v_num=18, val_loss=0.172, bound_cond1_loss=0.000, bound_cond2_loss=0.000, bound_cond3_loss=0.000, bound_cond4_loss=0.000, time_cond_loss=0.143, phys_cond_loss=0.0252, train_loss=0.169]\n"
-     ]
     }
    ],
    "source": [
@@ -245,7 +14252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 5,
    "id": "c086c05f",
    "metadata": {},
    "outputs": [
@@ -253,20 +14260,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Plotting at t=0\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Plotting at t=0\n",
       "Plotting at t=0.5\n",
       "Plotting at t=1\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x600 with 6 Axes>"
       ]
@@ -276,7 +14277,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x600 with 6 Axes>"
       ]
@@ -286,7 +14287,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x600 with 6 Axes>"
       ]
@@ -296,13 +14297,10 @@
     }
    ],
    "source": [
-    "#plotter = Plotter()\n",
-    "\n",
+    "method='contourf'\n",
     "# plotting at fixed time t = 0.0\n",
     "print('Plotting at t=0')\n",
-    "#plotter.plot(pinn, fixed_variables={'t': 0.0})\n",
     "fixed_variables={'t': 0.0}\n",
-    "method='contourf'\n",
     "pts = pinn.problem.spatial_domain.sample(256, 'grid', variables=['x','y'])\n",
     "grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n",
     "fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))\n",
@@ -393,7 +14391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 6,
    "id": "33e43412",
    "metadata": {},
    "outputs": [],
@@ -426,7 +14424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "id": "f4bc6be2",
    "metadata": {},
    "outputs": [
@@ -440,11 +14438,14032 @@
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 999: 100%|██████████| 1/1 [00:00<00:00,  7.45it/s, v_num=19, val_loss=9.22e-7, bound_cond1_loss=1.95e-15, bound_cond2_loss=0.000, bound_cond3_loss=2.1e-15, bound_cond4_loss=0.000, time_cond_loss=0.000, phys_cond_loss=8.58e-7, train_loss=8.58e-7]  "
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "689e6742159e465d83f030291d3e5c48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9eda1d06d1d84b96ab4f2645ac8fabe6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52b2e73e7d6c4251902e13df66352eb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9481035286e4902855137337ed94f8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f78accaf0074659aefd3115d9d567da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "854d4977764a40bda72deb8e9c0fb016",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "698934f06c8344dcbbc804eaf3297dcb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "888cfa4ffb624615b83a40e29e199a37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1e98aff63cd46db89dda070e3ea10e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0be5ed3cdac54ed08c2edc140491ccbf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c144a00e50540a8acd3d68676df8f7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca033b0aa5284a7b87395ffee2b30704",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0502b9d3f6642b8b3fc67ac23547d7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97a0df9857cd4318983402ab8e78c607",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afe3c18f7dad4abba3b928f39f82d54a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c7d285ca0fc4a6290782bd0a73de505",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c995839d90d348aeb131a98904779cb8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2993134646e470b8d88356a556a0cf1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0034743a00142d6a5c9c44e8f7c8801",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e49ecb8fdc04160a7a9745cbbf63a7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff541a930ad44ac49fad68452d1af1aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90aada1dc79f48c0b9b33873494ffd98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "552d68347a9a4e54b2c708396680662a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89637888a1d74033913594980a4f6401",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef222e209fa449b8b17d4544e0823fbb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc4d600ef97240ba9c3475824009deac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80ebdfcb09d64eac9fbd47225af4a1a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d92e40f67ca4c91818f99d9ddc76b73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a289d56cbb334f2b8df590c5cc1d3adb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0ac3c58e8334eae97079dbd297672d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "535def4c38444906844ebe5af44de3c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18174ed41b784b1193fa06fc01151adf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c324ee995b3d4743b23d69dd533d4152",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "170db58345be449985bcec745db1cfb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "105c4b5705014c85801428b2c4956f77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48ccc5a4e2744b83b1ae97a1d7878bec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d887fd206c794329a17dede198a4593d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df38a39e11374af78048c61dcd932412",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8549dd36adfa4a77b41c89b46fedc1ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ca2037d22dd459cbb0dc8fe2fbfb6b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5130fa47a06548debcde6766b83a1b07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc7285b79e1a4de6b9f8f45d2e3b033f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f45fe65c33941beb8a1be2e2b4ceee8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "552745e750a0463a8a3e98bf4aa1ca6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6dadae0762684325b59bf59d5a074fea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99e313d77b904c9b92f8d7eeac41ea05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5f664c11a0649d496aa56d6d5827c69",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17e5b2d06767473ba8b766d35e7b7758",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d6301900a674b86a20d83f1241749d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "414fed9395494bb7b161f5b23d9cebf1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f87969cb34e141fd8a061fda57a76641",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c75adecab9f74273bf228f5d8923443b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7995fd17875543fcbde1c360d5c44fd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eab58ad4aa0a428eb85fb37106ad419e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fc588e0b9824a0cbaee2f37f13e8a61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "213cd44d69ff4cd88e5a27177950fc06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa1630952ffe4ca98332da21a0fc39d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3820be42f72d4053b25b839937c11235",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb0db273bd66463e8284b24cfc704aff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c249698bc3fe44ccbe13da90a23e77c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "533c6e32cce44421a100e681413833cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e10f48edf2554f78bbff79298888536c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b4d1aca8aa242aebb673fb047ac25ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19f9a3fbe40d48eab2a52a6620e0ad19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "857bb7f7803846569a63aa22e5e65d88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b5b14fc4c064c2cac0dfc30f8fbf4c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea5374d91a154e31879e8e57b5dc3a2f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f003d1212e3438488150a0350b32767",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3389dd75f7d4cb6b6129bfdb159a902",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36724ae02d8d428f9137e95a1499c295",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8fa1310f6194f47a4c450cc106d539e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18612d3a7e564d27b23bab4d97516a37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31e0937ef41d4b479077cb55ffb95624",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52e255c6ecbe48d78db671a2282e2903",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a2e44c94ddd4605b5cc4f6f3e9c8005",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6347a7c3e941466fb4f992fe40215a6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1e7b8ef21f343de890ee64187efeafe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c8e00c2000e4431b1db3523b8547722",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "015ea601cc90463b9a290968062382b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a240e6de00144522a45eb993580a89a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a9fcffbf1d7473588cc71fc2fb6b254",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb11c67f2ec142b8a0ae81ab181394d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ebdc975b9364c8dab4d3c8db9ff1371",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96d2ebf4d09845a4a6006154f16df606",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81b1b1454c1643458f661dd43f3a7453",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12f266333188498581ec647cbf2e1129",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d64e8206e144103b8327a0f45e6b609",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e33a97501ff485e881c6987babc5545",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a61da0942624213a2a0e155e31b3a6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c379c521eaa4e6c95216c0d211a9c5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca67ec0d836447f8838f470192864bd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e9882a95d2d4949a4c577fa8eed91c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "531023449d7647bea0739048a2f52cb2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1648b93d39149ccabc99634b034b361",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ac1bc3863ef48b1ae7a8f825b1d8255",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81a9b1cece904cdf8e1b57d5c5fe6ff5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4c2d488288e49a8b749d9c32dd9e1d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4080c687656b47f7b583a2088a6d8a32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "075b2c8afe7d40a68cd6d86828391c3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "906a60d295fe4c5582d4eb94a11abd24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33fe03ce4f804149b3cd649d2a16e8ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2223243e99942c48e1ff64254287967",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ed581f0116a4a3fa9168a7400f035a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b563f5e177ad47b78710c68c371db89d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a67d72d8a0574ee4809d2a5895ea9de0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a277234b1cd04907a5007949aa1d16fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3153143435044b8b9145cd23f149ee68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2c908d9c90b4781838086be04c07978",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df0d22be4e514e76b83a05caabbaac76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d04cd6850564259b71741bb998dfe6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "463b14824a8042658fb81a108b508700",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c958b1839ad4d93bba81203b8247600",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "036350bbddf64ba09a55431265b2f9fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1c9e855dec548cf9be3fc40501efb1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cab3c7b88e58428db5cd1c7b4a0ef764",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca9a6e20d4b04341826f439c9deb0bce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "311cba03863c4356b5550119afdd42ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed951868836b4bc99ff208449c893ef7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e782cc64bf8456ab37335142a3022c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "228fed715d624441ac23e76b1a7bc11e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35ec178b98724138991b72c571b742dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51ed26160c9b45298e4da6bf9a54c4f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1d4ccfacba84cd0a8f912ebda95f9ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ad5bd2356fe46cb920365d15c9b06bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc8f0b0c38344374925d2f3a1847ee5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da87300c082d4ccf8bad570c7e4ca1ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66177276b25e481e8107cfa1e2de623a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bb8f7933b2e49a081000199a64e491c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c2ed1c230db4484b1a63e233d09c89c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5abd21ad121b4149b7c35de2aa8381ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d833a13de30549acb504c7a705b088fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1959a35096dc441f85fd3c827bc7e101",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "665e52615db5464baaf25705a883b68d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0366204a5a541a8b0720905f65c020d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "610f9f37c577476d978ad5cf1d25f33a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95179fcf30f14806b17b3c6aa8a31149",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "92adae4c8bba4ae1a289a51b62241d53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49803639b46f42819e0c48724981b8aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9ec0d3fd0344fb59758d7f7f7f29309",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cdececf90c434896b68ed8f87d15be88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a37e6fe94fe04065955158aa35fc67ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebea31676fb642e5b63d0f61b845921f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07e97d7db0994cf490afdf64c108de50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb9424bbf5bd40bfa3cb5a61132323fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b26da94943043559d17fcff55a5d16f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "520a6c269ab54dd19b852ead4a8c3ad2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee8660e7450147cfbdc6595fb31fdbc5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c9b081bcdaa4011a41aca7085b74a18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e02f17fc07084a6898b5bb5cba730f17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a26d6c30eb5f42b5b8c8a431adc21000",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b4b33881bbb408f906f2d4ed2c5746c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96290537342a456cab91e2d5b8dc4257",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b98ea8d3aea42d2b89b5e232c16f363",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d7b15262cad443fb0c143b0f135b9a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d07f629294bc4200b86e5c35f443a563",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c389f83c872b402fab44c73483a4fcba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd7dcec92ed841d3add7e3ad1b2f1d4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6989dcebac5f411aac838696dd3de0a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e83ed2861994f8283f36cd19f3d8ccd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0e037161ab4465eb640b3231303be9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4791ded098249879d042994f74f4caa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a7f753fcbee479d82d9b7e0763ebcc3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31558551478a4fe492ef7f9f1e32bb9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74ed99282a6147e1a48e48347c32a17b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1637cf148894338a36a8b5e2f32e0bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "525e0300ebab452b91b942870c2ad04d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "364a94944f3f440ca85e8529fe734602",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e8557f4510b44a0aaaf4c8f72e09421",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcd96e5e693e4ae5ab1dd45a1c006580",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37e5f237c65e4ee39d737ea05464dd17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6381674f3c9847468acfd46b07cfcf13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12cc5f55b8ab49428cde7ed7fa799d5f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc304bd038a14dc88139a653c744515b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acb749faa0b046de9f27eb108ccd5520",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80cc04bf96894feca85c95e3b4d4edde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2a83f6941974e25a4b2403cbccd0d1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1b6b36664384361bce0a466eaa4ad2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b38a2044b614ca18f1a88b4ac9cdc81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcad359cbf2c44008244a2e69c7e9153",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5e038505d784d98ac6d32454d12cd68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee9385e9a08149598a20627ee8b48812",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f068e1dacbc46509e11a4943934899d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f9a7c4c6abc4424ac9f17e16b994953",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9584ee495f940db8210f78b56871825",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b57b1175fb914008be488614efd82146",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c4c5051053b4bd4bedeabbe4c6e25cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b963f89b418f4f56a45533e0753dbb97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23742e50b78f40f891f44e5c8fa66126",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7150813a0f1484d8a428a771a6bf384",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1328b43bfd0d460ea37c401005288dcb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9aadf4c8baa4afd82de342b7ba5eba8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9964ca0305874773bc9154a7c2a7f1e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00339bf6206243aeb779c4688e4170e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "372628c1da7a4ac486108d4e05dceb25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d0f98a32f4f481c8109d612648b4f6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa285443444e4afa9647d332178d45f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f849625983624f909dd716c780593e9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b0170ab5cc14b46986fbbf8bf217ab4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80ea12f56f264043abe6b5c3fec179d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6033e0239d9a4c30905178f13ec75fd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e0c9796d6664220a68f434b716f12e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e37affc23074b48be1516cef096a0e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dce65ca63f05436eb700f79c7517d467",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff8a6ec12f2e437487e6091a50c1e9b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b186d979b0ad4d5ab502e0ea98a84268",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6622fab4f954498b8203be254906f251",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c305e2d2ec66473481232336c18d2e18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5810cb549d6484c97038ddac617b130",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "980daf5bc70549a28142a3cc099603ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1db1886cffeb434daa62165813710310",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9abd0d255d984f738945bd8ea1099a9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "794ceb760398484c8fafed513047bcf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19ad7d443b5a4043984d29ae38836838",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7af4e0435d304ec396edfd805da6d957",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68bf18d51c66489599a67936e1ae3a11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99ffb74ee94e41c9ad91abb5bba64e32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "166d5423a65049edbd7755310930f633",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cac53c7a0c144b8f8e0516f9a09d8dd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbb0911f5efa40379f2ab48a3881e89c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da50ae0f007c47cf8f54ddcb419e2e13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4880e9d01b24c5ba7597557f4c3a007",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9da39665ec6d4e429c3cd22d3bb08593",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b214d807f60642e4beb64150dd9916eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9328b624fe294f53b32581c5feb59e38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c0911dafa6a4fe293abb8ad14dc448f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a00967381074a429711869e9ee028ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "580b8628d6fb4c0995dfa791fb703c6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "553a3a2c6a5d4099a9b48202e0df777d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95bea3ef68c34ac99bef3196727b5466",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8a73145c4bf449286d1e7a7c3fba556",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c28cfea45144902822d9b87b43b4d16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d45fdba9e7241af8b77ede599be7ff9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de38a0e0b0c1478fab37ae5e620c9e17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c954b2af71dc4f559130deb7010972eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "957d9a2de8a040f399438704b1ca1519",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de0a9a742e454aa9a2a408eb08a854ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71ba02e5cd9d4bbabd4699faa5814d8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97073edc2d564d81ae1d7645aa94281f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83d17c49d4c4448b85f99fa5a7539eaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37b8df01b4f14c7cae23f456dba588bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a04ee86edd044c58e7a8811ac6df082",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c86be78b8374d20893f3f74cea563de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24bb124d97434c9ea92f1b51171ce87d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e7f1a5ea401476f9f0639178c03003b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "375870a8218e4135881166bbfa892932",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "388068d4795e4658b4fe989925093045",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "228a7aa4992d4a02b0fbcfbdcea49e90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6eb3f56e281f410bbbf17f0d0313621d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac33808adbd142088c9f1de7db9a7661",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcc7d3196ba94b6eb3230e59e0098667",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8898c4ac2dce459095f0364e3f74e19f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6fb5d9cba964c55a442e622c0fde293",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3960449d65c04dc2afa7c36016c4ddd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f2d5f1fc3a140f5a4c0ff5e233aae49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19b73450adf44a90875d1fdfcf9688d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76e742668c0f447fb52d6817ae43c732",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10489b09f2714f29adb89570c0635e1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7c84ffd1e674b658f75151d36033fac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10b73e1bceb8454fb6895a028ecbc488",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "535cf32545064ca791c846dede26ce19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf78262af62c4054a7be5b5d5102e04d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a1660ef49ff41ef82b16fc73048f585",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90883bf28a7048f99f4df1092d507fd1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c66c4c5774a4a9e8eb31fa6e82901e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74387d90875f494d8081c4e7bd3a7b00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6534f3b76674704a9dd6d93609ef0da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1d670ea7ce340b8920d1e5750c52be8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19188bba3040487197df6c142a518089",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59b6e69b463742138494598fe7a7e88d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f95407f9e27147ccb04bf5285799306c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01ca78cc77e643d8a81ef0f066ef39f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7da3d984ec8646a1aa7c9d58c7fae01b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "406ad9df9c02480abc4ad3c7e01ee805",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33cb2ad33529486d97220c6d3d609c42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c17425bacf09498a8e02d942cc857fe2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8a9b68fe2634aa2af5b5cfedc6f4d88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "806d148616ba4e9dbda036ba24b11084",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c075b0212b914549bcb267516273d8a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb89a7b75b554f5b9addd634848d9d4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe99ead011b24d38bd7b910849caed7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3764a2627cd9435bb437f8658e457811",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c96086e652fc4bdea8dbbbd3942bcf0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0132b312ac1746f0af6fe7ba20713de6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "594c101eb7ba4f08b887c3d768081a29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1501cc3d0ef441dbdb148e1ee3c8e2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2aab08ed6af74346a8b5caa42f5d168e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70a997e9a2da4510ae66a9d51a27dc71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ede48e67b17c48c79d503d015481f494",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc6b71f3fc274fdb84e66f8b1592b6c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "262a1a2d1a244622a8649ef63f931651",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c98b368c84524573a314137a9f8aa4ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "154335e951eb4c8da3b480adcf6419df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3884b644c97540a29cc020849f53cf4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4022759dca9f49699f42481c1efe7c45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1aea47022bf49d285bfd7a0cd2e7b03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eff1ca12abe449459003a8c9109dee1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50100dca6a404754befc7252c16437a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d94069f7d96c41f0a06eaac02b4dcae4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40a5f6e74778441f995a98d31821e321",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b8066896da148d4b00b2e6859ad3bbe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a9259e104f549879396d606d18947bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73c620f4109d4a02af280bf7b84a74e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "491ecb068b93480fb9824fa3cd4e77de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c9588af99b74a2387aa0254fc72e390",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ad1dcc9062c4633b37787a18580a223",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81dd2a844c5e409d9c95d6f4696ecccb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "92405c5b1e224e10aca943294fcf3733",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bae7b607ff5544fa8f0a2bea2fe6eb7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ecfcd633f6ae456e9be73979219bd44f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58856a26ce244783b08c79efdc30130b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "209be7bb114845aeb04f792d9f59fb3a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f771abf53114bcfac9137837730825e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d36f37cb1b24a5b97b3bfbd9c4323ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fc2031e8c1f4861b8051bb5db58ddde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b8c43f13d884e3ca20011a5937edbd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a438a11ce6e415a8ff68fdeb9f56b21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a17793d5b934a1fa83e15921b4811b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be18e2fab87440e1ba52a1c085003df9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4132cbfaff804f17b090dc756f274fb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "831e06682fb2459fb9731505a78c3bb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9957419c63284732a22730f8e46a822a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1765e341b6e64831b71890b49ae2d824",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf74e72805404aceadeb85e37453b47a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "158cdf09bb20491ba21f9974d671c276",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c18d31ef8ecc4b02ba66b071038f1ec5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c0eddf822c44a5c8628f18823d5a71c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c8a462e182348019bb9b8c39c2ed989",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2ee1110d2704e96944d722abd10647d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d6e3623975844f78ca6abbee3efd47d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f722e81dfbc242cabb96033061be1f3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bf526b3b332452fb9c0c9440a1afb6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3ec60e23c94402fbcace185adabae9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdb3afa6b8e94e1b9224bb3b2006da86",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3048bb1b2be345beb9327c0d83920915",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cdfb0ac9f6d04d89bf9604a7d9875435",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1eed2515c40143fabff4d90ea4024cf8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23748a342aa6488dbee5143305b6c7d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2d75c164bd244b4bad3893eef2d70e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fe1d034177c496f88f065172c41d545",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af404596076d458b8337b0e5d3acb36e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f642da832ad4b96821c28c011857b77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffd6d62bc1f441ab96b50e47bcc3e9be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89c0697eeb4446feac79d31abc1381fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcb9f364893642d2acbd49a2d5d9f174",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2740a342442b4ef28f944ade7c06be84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a208e811284d4ac6a4bd40884c04d09f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c34ac7b9903040c2bd6fa77adab5870c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5c25d845b5646738413caf550419aff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b3b672c00734a6d8a2b71d072afb984",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0473a6a1270d4b1ba17fd6845cf35218",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53782d7b68e945a0ba2104f48aed40d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3675e8fa01b4c42b26b821647d6b117",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0b771df30c141789e8830678266045c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b034fc60c4d74c7e9efaf8d9dfa186bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8a2a0ace99a4335b18d641d79fd3011",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b488d219a2764a299a5e91c21880c8ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f5a9164d7de40ff929954ca831c5bd3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40475324434b443f9bfdc2e855815cef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13cfbd48f6544957923ca14c8a3fc28d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80bf7d944fae4a62a1195b88f51a5ae3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df7b1314a3044ae1b1f87e2b2d89eeed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c20af775e0ce4c4384e794b17fb8b197",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82d79a74c3424da198e89ffc07b16280",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0268817a112c4987bb4f2b44986672f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c59bfa48bfbc47858e4471cc3260390e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e5195447cd84c349a9f12c12d432ba6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e648cff6bae4b85baca4360acb2e202",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f71c1fcd9893488f981da7d690f5d410",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aacdbeae50d54f9d950d5e4d33637083",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83ebd7d73045413cb24916cb399adbf4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67858145d03243e7b5c0c0be31656a1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32116d5d7bb44b74951501704d834492",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6a8fef8c881454fad41c0ba5844ebc0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68ac34f3df6041f08797c7a4aa3c40fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b6df67b6a444963aadd9877c86e580b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1279988053a041ec8b36e4f723eee423",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a3a955f713040a9b2604a0786a0e7ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8417352139d0467daa1a57c30f1020a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c2c39bb7d66494dadd3ef5da8938492",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "807957bbf8ad4bc1a447083b0598d669",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "870538a7574e468d82916f699e9c2faf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20de7abf4539420e84b343f4f31a0164",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42c44f72b188478abdeb4f33ae81ae95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bb20bb3cf564da59055e5e9abb7bf21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb2ed6e9b8c74314b949a5b21c70c47e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3a3a690eeec49d88e7db7406e547840",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20fa114bf47d422fa58c20437814a287",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4c344a8f0934f759a2112474a6b547c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "92c5d3f009ae45fb9ffa9d15bdb3567c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e27ce48e320c43f5a2fa02d899598ea3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9af71789e53445f3a381d0c305ae6fde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c5c6f3244e340f2b8fa0a548a48fc80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c59d78b85871466085fbe33b5fabee9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4e01abea070411583e620b600a6c7cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b887141e009246ba86b60d29c7335373",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95daa1a55e3b4247bcb3536134c2b4ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46654dc3518d4906a81dc1fabf01d69e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d58f020b0fe84193a51949614650aac3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51afa4aaa54248a98c6190e2f9a07919",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19dcd8d26b60418b9b5c8a44a2200f1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3150add998804c23b0407d3bd45c30b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f201d7661d6a4694a96c74cc5a7575a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ad89dc489314f36b40e1ebd64ad6afe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb1d17b1d7ef488891efb40c3147b712",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6eb4f6317d1944d0a02690323c8e03c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bf783176eb943ee930d7d4b68a2126c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1636b5d6304649d8b844ced3071e757d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cd4f79da4ff4f708485ba6d139988c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da5800ad665345dd984ec9c70b57208f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75fc0b22bbde49d895352e03533cb86b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e88875b391e4ba1a660dc189cb95adc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a0222adb96d43d4b5c905a43a488485",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09886aebcdbc4a40b4e658eedaac5ce0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2784202a5e394f109f9ed00dc312fab2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a2e0c3b8a9b40ce840ac69ba64a327c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a73d2a0bd21d475cbeaa87ebdcf73f61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90bac8b4beda49aab90ac9c3f225fc00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c305583324ea41299e3d365263cf3e13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abf716c7a24c4114aeaed0e4eee92425",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "255d58d28e8242a5bca0071d95051623",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f842d157e8d74a69bac0662a6ccf96e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8485e20baccc4bda84f2ae8f7250eaf4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b0a331d75474b0882ddc829a321ccbb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbb861ced2d444669d9d9d9599bf6a57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9052373d440476d83f4b33ed2154cfe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83c2495989aa4b658e8f4cce00601b96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb1e9442daaf4867917f00e9bae898c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab5b4fc8950c45859ade38cde5a14a42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f7ce7e8626745178f9b4eb792b3fa7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0e77a3b5b6a4e82ba1dc83f788faaeb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2b4191406994be9b13ff57224f7c11f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdbb602d97f842308ca407701a8d10a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25d7f91434c14a04b571b8d849fa561d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "855f5e4dbfe248dcb065f9e795efcd5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f42dfd4cc2104e799125acd79d44926e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15a6c07a91eb47108c9f9899484cd398",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ff116b0ca2f44ea9733d3bfa8fc9f2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8512b01c899e45c3ac28d56ee7175da0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10cfd6750e0e487c9bd1b8a5e208fe6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d38180616a4a4ff781be37c14fc220db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b835b6676de044999b2882938a4c86db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ca44b0f71ef4490907b0b9732d6cb34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67f4085cd11d4c10bbabf1fede017c4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f299e66ea2294b0e901cda005331d7b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d306aef68ba4e82884d713f639410a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d5d71d527f1443baf8f444473f9e79e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26dd36baad094af299967488e3996be9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58eae40facaf4777a49bc764613d68d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "347b2a546047494fa84c6b803276948d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9ee0a77b80a4723b215e9de2671d820",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e0f70b6a58141f4a3d1d834efafa2a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4029278c089450788aa764541de7a36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d096e582f8e4480af716f9f624fe858",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76bcc40c12f74e818831c023f9ec1ec0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfc25960b43f424fbc6ecf53f5bd0e0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2208c2c6bbac4d9bb339889d4a3fb938",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5259dab3ac74a0597bc99578c4b2967",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10f12c154a8c457a820b06436db07f56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3895c2b630bf4bfc89b2d4ace636e52b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "810f9ef7d4624a2eb1c18c0ac091c571",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9436dd978274bef85e2e928c04c7caf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19606a4931314a7f8edeb80d6c18df0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b63362376ac74c6bb57845814615e0e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a174d674c4647d8892a22149350b518",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e96e60d211243dba46c54b8363ba707",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd823e87ffaa462daba1fd145d8f1dbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "186a106ff7e84a4e8a6e900b200282d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9aa7b5ac77bd4bacbd9024b83aea153c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "332ec8d1fb31402fb8cc1e80fa8336eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba1d8d44ac2d4319a9a08f6db15f0c6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3cce14fc36a423090f026413fa0389d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f86752e5aab4faeb45506a90ddb58f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba1ebe79322e4dc6bec941473c50941d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59dccaddfb9c4a36a136527b63d31540",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f0246dd4f6d4f54b152400298528e6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab8fc6f95b0a44ec952e3d1799894fbf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31b94ebb4f794351acfeec8197048002",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5321253386174cdfbc2e0ab8ad946a49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fade64d23004b9fa582cc1833d1ce45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf6380d265f647e28aebe422a00211aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de0cae3999c049e5aa14f3b2c04dd165",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78217c31ef854d9e9f15c6113ee6a3f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b64ab4d847a46beb5e9fa6207390d75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45d9303b28b54185b8455e8e0494162f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82690207f6b447a09364bdc76c9bf027",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09427528872c49278919c069825e5133",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd4e95e5fcdd49388ec700b37a2a9e2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cb15e0bb80b40708c4d64b978f1df72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16fdc82126df4e07ba61f941fbc0c2c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc1ccb82dc5448f181b9b7e357a77398",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc434bdebd9b41e7928a8160364918c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c51cf41d41264dd687cb6bea8e59c80d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93f7af0f848b419da520dc02b6b1efad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a31db8256564562842d2c469138af05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4da0a4248cba45f19d57f04092a3feae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd83210bff4e49c795e3b7e0bb1924a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20eff38e50024bb5a16818563df431d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7bf8c4892f54bc487233b5dca5e4bb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fd9f9a2d00943069f28d5cef45d1d1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91856c26f94145a494a21955f83e9987",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37bffbfce4a54f02a3e55f8caa2ffadb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e6413f3722848f28e915ae61d16b8ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c1aa1776494414c81ba420c47127cfb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc39e5aa474147aca7890c8b24e9d7af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d326398c971a4efd9f9bfb556ecb5b39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8dc06e4b1554de78370320f4c71eba3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f733889232cc4ed2b9f108f0d6d21331",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7648dbc9fc24f10aeb10425c3739975",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76dea5ccd7914a51ad72e92e59624d64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcd84619dbae4b9e8cbb7a9a2342a99f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc99014b2a6548469ccdf27233615b37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e461d40e3ed3447fadea726b5bf9c706",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cadd6f89b602407e9252b6fd487a20fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8e8441653f64c40a4ff0084a747bb9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b67050ff8284275bee2dee721e4c760",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5665f492f1c940c188ab2b9e65b7d732",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0db4c04264034d05b160b590fbf23678",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f738bf54e754ed98ffcc9eaf731e02b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89901234c8ff4c919afc08498894ca22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1b10722eca542e1ab1f246621819fb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ad75cd043c6496ea96fdb830646e43b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c6a93b35a6649dd8a1b2f056b7bc541",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94f75c2f2a014023961c6cf9dfe274f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f4b6b9ba6504ea4ab72016dd5bbcf3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3803f99d33c84dd8834dc1b91554e7e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d541cc5eddb42c4a3861d8074ac7784",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ea1c47a262c4b2e8984727c0ed55a68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "689eecfb62c9422bb413a563d4015fcc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40f3799ec81f480083f58d9dc4d5f20c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49cee3bffd0d4022be525195fe92e8f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07ceefaa24e640ce94393b8586c14912",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a347f250bba447b08cd1bac5b283d297",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4020abb71f1b43fa99fc321f3d564be3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f62ebe10276744ba85424fce747e2ea8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0820d1810f3d4ed79b765eeeafb48595",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "efb25c028d804207b297be0d982a4a78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5700f7d5575143d0ba4205d6b20e846e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e90795cdd72405cabcc4da20926db24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16c949d1973041548b23391dfad1dba1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e4daf874fa146e2840a5f046c29f8a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "faaf10d705e3432e863fa1e6642825ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85bfedac88c94824b22f4e51ac8254ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf36b4c8e45541678ab3234abfe2b243",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46c806105a2f47b79f82b0ede02f2f24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72e6a86d334e4c158e1a4186c33d47c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f5832504f344ca8a4035d89a45400f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91fe680d89c5413f91b86fa20239443d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38e454c99a164c55ab65944c0425a071",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d611944cfed44638b19d84165f689e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1736198341ad4038b016ba39cb6cf369",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd6cb77471cb4861a2158b805944cae0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fa55376b1b943038d2ed3bf704e8379",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b09c71baf7ac4bcc9e5f25261e4a4c2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cef953c7dfe428581ad93a37feff088",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f384eb441dfc4a43a4e2480c20fc006c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9d68f73c2194b98adbe77fa5554a8b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5e4099b932e4388b9046d303a6aeb9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cfa0830d8684673be043cb90d63e455",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2f90fde40064754908b0c5af2d82064",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed7934d26f0e4daebb8711fcd015b45d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79ddde38982446d7a450c19443bcd0c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6856a5068b134034a444699f3430b6a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04755aef3e644813b3c93ceb54376459",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9829506c39474f71b36ff4d46ea44b74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ff29a38bd7146b6b48a9ce90b7c71fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f6ad77f04ab485d9b0e4491475a6ef0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f570e1e45b54934a22c868b0b41cbd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51d2e550434f451b96cd776a0ce4a4a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a699d31cff9741e3add61299dc648d5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42717def3ad343208923dd1bce76cf0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8287a7dfceb94372ada1b8fc2f400146",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00f32c7a38ea431988f10c9acf9f5e81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0b9acdb58304dfbb76bd2ca72d8f4f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04763f53a83d459daa20956ecf1b21b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "554159dc609643cda339767c6412b28c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6311e08835c64bbd95a7be9f97114429",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0643cf5c99b416187685b055093c60f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d97a3a048c644126991d6c68ad8ed196",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cd5f238fa0c4ae2b8c81b46bd37c604",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8b3644d89f945a5a2e013b6806ecf6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f8909eb9bba4b1f86d7cbec2d6c8afa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4199c0ddad0470995c632d3061614ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a1cd8ac53af40db8c0aeb40bb77dc4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0829c7e4d3ca4007a7943169f84656f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53266932b8624ac1bc9992f453e41502",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c32f850dffb542bca13b4ad7c0f6fb15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9bb18d9809847c1b6f7094ec174e9bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b196e9691e29452cba0b832ba2a56124",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6758459836dd4eae8133e7cf8cae4900",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a35eafab9bd04a369e135fc9c36f92b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "904eb5d9dd594f14a1c98338e6463318",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47bcecbc5ead4ddeb524658e4abf9053",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e83a142659d47b9b02686946942fcb8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e2bd38960bc4e6e94dde9901229e085",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d77b39a22d743fbb246e12aba719aca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "736cfa25fa8344eb9d4eb546252a3d43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81449188e2fd42c9aae16f7ec1343f0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adfed624c3304f49bb585350cebc13d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3b988feda784679ba9847d55c84fd5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0fb0b1c221c46bdab120f1f27334c0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78a19f75b9ed42b5b28dad88ea3cd4a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bf09bfe24bf41ca81dc7d7e0e74eb2d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa8cda04d4a947759068a7f7800264bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a448e31fcad34adf9082be9c01093aee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11d86880bdca4e09a612469cd9aedc09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2209955b9dc4cda88acf3a3ad2503bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d38058b0f8a46479e0249f5c23aa0af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03ae0df97ef74e49ab2c1375476b4fe4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8afdb541daae49e48ea27999452509cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f7d1bba8aaa494e9c74227bccaf7e37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94929a4f5659422c8374e54bf4762895",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6784d3736c294d3fa23bebbc9a50803e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea9a194d5aaa4459a4569d5e327acfbb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae17dfa326484293acf863b51dd0952c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42c2b004aa574b8887c1a081d308e4d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b260161a11a4010987ecccac7e29cd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "689b07e0995b4bada6d7b412d710a308",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "231b79084fe9432fb83556483b9ebac4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4be0a02b37c45d8bb44e3d2d0bb8433",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8594766e699141f4bc5631f00e56e390",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f930b2b983cf4b199a4f12cdaa86e860",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f94557008ad54ffdacecc707938b4f25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29bdaf3124d9460c8a576e340f871507",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b55e50f0d5cd4a20b5152b5488582c71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84477bc4fe5d44e0a3ac17a9bf9f18c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a55fab7bf5644a10a6c5fa0fb6dc5eb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14963358404d40bb8aa093e1f9ce3a30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "269ed24024ca4ff283355f0a5e140641",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fa0f22a805842b7a9f311894bca71b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b679c5a9be04a88a7fc3002d7069aa6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f0c76e14a954c8d9f7449dfce6adbad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94fb35fbdb394ce4a462989bd9c35d80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cb375eaf1344b088517a45db34450bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8966fc1b8ada46bfbf265fb8035038d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "163aadaa011243dcb9bc9143bf10f817",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3349465d836484ab3cbf0deaba8d522",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9008ea3fe4e948c2b45ba07bf803db93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a4b0f2c979340e1a5c06df135176ad3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a60a96d485ae4c4c80f7adfa2fda4bff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e12fb98cd74495796f1be9b4f9ce6fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5b89c2fa20242faa6ebf7faa72b4250",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0464b807ccad4a91acaca37534ab43e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79b57f753eb446268cb5794f2e573733",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "315076d676d54bfe8a855daa5e9a8af2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c935f1b58c24435892ebabf508f4074",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bcd1d26f94bd4d5090a68c63d219379c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36cd44595ca64f04a064cc50d404ad66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1107aa53520f4b1489ce37efab907c60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4602e678104d41288e150732ddfa370d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93801473cea44b51adcbe42ef0938c35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88b5205b2fb84b16baed1d5fbccbb884",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cdcf046bd294a1da275d27152700147",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc46b4458277480dbdb87990cd8b97a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e169f4db48724193b8e83a14d1df4da5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0312baa8f1f4fd484f2e3cc850dd411",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f08049f1674441e894185ce655d4c9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8103fbc488784520b2b1f3ccce5f1e42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab338a30181f4b58b9ebf9de85b11e31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fb498f4266b433195ea6e9fb6bcf20e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1e7942c66cd4fdd92ca94b68c277422",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdf4930e02eb42c7a7efa591732ed033",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a820e521cce4829835bacb508dc0889",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18066f2f48c943e4a56fdef8dfae4791",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8575a7a82ee846a8a5f4c185ce1fe189",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cc659a2c6a94dffb7667f521add6e8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fe5d7721c2742c094050a35384c08f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b4e61b0458449fd955816bef6322b91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3da39e8907464b4bae24466b76c6a8cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0e47319cf204f3bb2bf86808754e65b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b9387eb06ab4d56a9dc6318677d044e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c58ec87f72f4152a9c7cca376fb81d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c57784c57274551b72b8062688a0a6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c19461662fa4de196d7a6374c3123f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe5e1959ba374fc7a1ba96921fbb9198",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6abb6ea9405f40b8b211cd9a7fa53054",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e911c3e72f2489a8fce9ab85cf7fd2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e8e634c8853418183da5d8c5864ae28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa39961c47304ad1b6501f9f7423dde8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e660957a60044828e21745a7c13771a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d00ec8602ca24baf9c6e24a1c6a6a4ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b7cfe5a19d045e985b7110e6cb163cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b774623fd38e4c6492089ed8e42db1e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "321246ee350f49378bee06f8dede7f06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b04490f97f8a4074832491ac90985cd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41ffdb1d7a204a4e88efa4d2129fd4c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7600eb2615d54e4b80939dbb00a99b74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd3ff0ebf6a54e16a63f5bedc0e982dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7670086cf08f4035aaeb0b7674afb8cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3393fc0a3cad4968b61c634c55ef88f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdc99ba1c0744c75a02a47837e98de77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99a58cd12f704a9eb47b0b65b9c675ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e15847435594476f8e9c41245745a186",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "640907f2372a410ab0331f175c177330",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5b48ac7fb0a47558eb6874ed5d0c46f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "589b1a4ae5af41ebb7518049a53fef04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da092374eae842be813f06bd66b5b5c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdd620aba7d949d987056f05070f1801",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "744b6034d00d4c3c91570fd05e391f23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fa2f2a5404e481ead5c516cb87f7555",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e25077018bc408ba047645d61e6b535",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9aeaf82d9b3e477ca74e35df5c0de983",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c13cba54e7a54ba4a93f2fad8fd13e78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78f9a96bc82741a19641879d451e13f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e6590e357bf40389b68920b9072f852",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2226dfd76ee74b1e853f54015cc4c33c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80d4f49dbe79450ebe12c5e15e193d26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c441516a792d42328142519d5132a6e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4060f2bcd1164579a97c9ef03cde96f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e6ed7bf1a0ec4ed5a2820225ff0da48f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43969bedc79f4a7d805cd992ee5fa8ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54c9960bd54f4163b9089068e8de8c1a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b15073f405c4681b92fa2a241947af5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fed6e81088e94c06a3d1bb15207e07c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f04301662d74428bb85f4cccd1d501b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf3d2f500a504f0cb80789aa6120847a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15c1262ab08842e39f76d17278bce138",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0fa03a717fc4dc3b0143e0baab424ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01c1a5f14f864d17a6d04e5cb9ec1102",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "680486b0fb3d4ddca755187e2273e4b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ad1340e83b043e69bb7fb9900a4d63c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "460e8d1d427b44da80622f5bfabf46f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a18e5604db54ec1a0530817c2965ea7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ebf9341ffb241c18df22e7e10c5752b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11413497cfbb49e88a61bc2fa33f365a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52f0539f4cb84ffab8138e430705da91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c747b0785852440e8a0232a2c7bcc5d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "093194d28e024607984f12078dc9d618",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb57a108108f46bdb6fae7d4b14e5442",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51e2b55255df4151b616ad6c2e467a4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c8ef326674b4b3c899924a4a8d09754",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f66158d2b2940779f8b8754ae08364d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b88d458111347f284cae9a95ed92537",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "658a1957739e4b8eb874e53a3e49558b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9bafce688f04a28a4093f2d375527b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98177256ccef4d5a9a6a88a8776a9803",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb1a425647e7411cb758a0c0c8c8abce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c35c0fdf82d64b628e24e2324267fef0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41ce9ca6b5c64a6ebd0e69106c163bd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "895a7b2a598c47bb8bf6292b53f8bb27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9453adfaf6bf4fafb838ab4dd0a9b720",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05cee80b1f9d48d995a137c84eaa201e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c02cf316c38e42f19d41b8d8338fe5a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95444d4af2ab4c28b567846f4acd29ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13303387ca2d4d329540142051c81be6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "341ad9086f924d35aaf7e571778cd9bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2526204a1d984dd781f181a500b55f29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a02b38b00c474634afe2ba5462225f00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e99032c952b34bd39ba66c4c8e2d2b3a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f10f85a5e0c4b498942bd8bc8a56859",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3290ce2c383746f695d477af432773dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e22acea063f2497b9bfd522ef087fe23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ade11b6850714e6d9187a25e5cedcdb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02872993401142ecb91ce790b5484d30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7e4619a01d6464fbe8921b118938f0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2978252246943978d3a05a3374032ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67dd7e8ce0954267b2c52f55402df4d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "deadb5b271f84aab9cdd466a239929dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8eed4503bba940cbbb9465bed3285339",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65c83cdda6804ac2be50d1444fec16f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61106d528ea74daba99fa26ec7ad02bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7421ae5b04824c558dad6cc556eff26e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1081309bd9e84718b47e0fcab8d1609c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0228d95656974fe6a27f8c16e5b02af1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11881107239c423f8a845be9168256fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12ee6c840f0b4eef96bff17e31a7aa48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cfb25da50bb144d39aef6b323e378dc2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17ce6628a6c542239f7573c6cbb76c2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "994f4214d9b24b93a2e88af22b8dea9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd226af4b7f14eda959e58080e39d3b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf6d52376272484e8e4372b116e9dcd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "397a6132734d4922a88b92a04aeb4b39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4ae9f0da5294303a3cfff56be9bd6f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "169446eb65d74534b6d6f1f01297e439",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91988122fd6046509eb1f5424279cc0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdfea6ff121b450aa71ad04d08847980",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6677dcf735d348238f6fce1ca15064f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3753302bbff44c32bc5c02d2f066dc9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ea551336552486eb50cf6f434962c09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a65fa32c99b4ad1bb5de72da821130d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "547f146b25284ea0bc049ce6f864b08a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a65a422295f40bab06d458f9c49dbea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "549cb61f4d6e4985b2d3058055ad806c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b72b9adcefa4e2390e04c5d53dab0a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83592484214449c48ebdf2040d18b1c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5352911fe3f342e1abfdef56097487cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f3c35f06b874daa8c55d1988dc38529",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7918e323dda408babbed892acea4c9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12d86b9440fc4413a2acca0d3a6065c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77bdc2ad32d8499793ed42f9642c4475",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ab82259288c42a1b052b683f3376dcd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c5a3ae5370a4ba5b603662bde8cf0db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8def0294e56948fc960b68b89630d542",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95bb1c610f434ca68987f6de14d9132a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa8cdedbf24d4565975ac689e3a670fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8184e21b3c854d5ba5f039f7321d6c97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b45501730fc442679ab4661a63a354ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74a41eb2f18e4c8998776baa9fee0191",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9c21ccda4ec4940a6e9279b808e25ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "442cc36d12dc423898c259287f540f96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df6d7f2df58e43d7a27c8939dadb3d39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6228c391f98240c2a456fd0bdc0a6c32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9131c360c9fe49789c8658827f1cd95e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "57fc0a10609947a0b673ffcd0c663f4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f652deddf1cf41ab93f845aaa6c20275",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06b0ad6d1fa34024bf6325a3518af52c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bc6627354a444bcb7c3e3d99f65bba7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28174b37056c45b69304b1838a1020ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8eadc158ff8c4286adcd74908726da7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fa1458e04764b91adffd9dc693d3dc2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b29ee55ace704873a88cb1543f928c77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1232e968d2e147c4b070d18bbf862389",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2dc587da99c4c398726c020ae178697",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de6f3da6304848a2b53229668ace88c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af8bba8a20c84b33b9f3cf57930c3a82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71a7cd0788094e1a9de485dfac440724",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5d68dd5ddb74cd087e4517a183cf77b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f73d2bd657443b4a994fda357ea6b51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed03948e0997484f9d3d56770d3cf41c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8af26383db242878be11b44ef7c2d5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3366952fa7594d75af474b960a155355",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89c6d83c5faa47e9b197d76adadb3795",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75af45b337d64d91a62ef2e940fe32f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d5e1910e6cf4c7ea95f4af5e56b30cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4eb0f426105b4efdabaade4e5cb2f509",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b465c738101a4fb0b5434cfd814d7f40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c38a72debafb46a99c99d740019c531f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d65014c280746cea0086a02c65c3bb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c39a8d049eb42a3a1c2ea6e20d70ab1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17ab180135834eb1a0ea647225d3268b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "877f180072d54f59b9d1909a0ef5e80a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "803157a689fe49b9a1bba36e19cc86aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60a009c721e147939b992e7a4c37d52b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14ef44a28e0f4a8882f462407681f042",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1aea5748bda4fc197e70cd4459d450f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87f2dd4b5e894ec289e20b5490789514",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6de3d0d85db46c7b34f75b26894d9e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f575459493446d2a28f82a28d94dacf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a2e42b902894b44b198af52e0647664",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6a9f197ab4540ca9cdaaf5c82c0d5b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "08a7d30fe322424e90e34c2ff0aaa9e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e59e0257ccae48a28626e53a2e54ef5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f32d88da15d34517b29b56f9c16eb28a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b05eaaf3fd74344b98dd01f096be57b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d062159463b745a783eaa6607e6c6e54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5756d30009a240d7aee14803c5ee7867",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f76064493b346c4aa26226614e1619c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4597dff9fbb441889a3a7c3d0c05487b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40193fa5354f46f2964cbc7c8d14443c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca0b3adfca944dda819804e571d58600",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4e30ed185f64c3e9734e5111ce356a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30cb83c1144f452ba3c35f2098528a03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "daafa2157a0a480e836e93f4fbb0c2c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97505be5a46a4c84bb3f8c5f24851a1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f452ae102ae469ab360c0618d47148b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da25d43f40024871a8b653b5f341c2c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a58dad486104ba5b8a7a33bfaddba6c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a36cb949f34d45198c79d4063de794d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0b99e8b697b4754a45172e60e3e191c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4038ac3605924b0aa1b0a953676f57b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6eacf365cc30469dab6b6108e50639e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28b10e3381394b21b4bff270e9d89c72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54e069900085450cbe472b4d198cade5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "471af5b57b024984b2e219b244c7db80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef347a660ca249749e6094936d9eb657",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4309b47ec3864ce38fa700113c3f3095",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82c3fc079f254d8aaf7380d5d9cff6c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b784c8bbcca348d9945aeb231ac92a7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e26c8ebd4a854b5d96c321105f7d33f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68d3fdda245e422b96e0f6f640eeb3b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15072f9fd8a849cabbbe484e4cf95d90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4013db59aeaf480ea9ad31fdc41e3e8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8835ac7bd10348baaed2999074b9b37f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3795175ef39844d9b16dd3bc1278b76d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8899279a19804a249e287cdda7aea642",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74d6f4c98baa439fbdec14558aaea162",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "350858f1d9fb4ecc8b7ca1832ec8ba47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c338fc52791749e1bd04ec94d496ecfb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d005ca1cddc847c3a9d00a9f770e825a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3426b969f5fd4458801e1e5d2776e900",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0cd5508345f4f40bf581c95af190312",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b0eb4bcffdd4b88800015ebefe1c81f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e0ef885a2a344ffb70c8a4f5f50559e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91961f19e3534f4a8cf2eccb2de4aa36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c57d22c50474eee8e6bb9392c553f4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3ee948c1fa946d28fda77add67e7fd0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "330332f99de543b7ba9ad827c74ca005",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af2cf06ef24041e5a79ac665f625182a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "831737fa676e4c40bb7eb6ba91ead096",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6711bc6ecc2b42939e351932259120f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ddb45c237b4141748e42716b7a06be88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2dd25a8183e4461d9f42e4b7652644d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ef2be213c634bd9b574a6fb72c4c2cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "661fe9cfcd4a4012b25944d824e9432d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80730f0d3c354f9e98da12c72c437d10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15b36bab22284a3c81456f0ffee50436",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "470a4b49dc3e438fb6713637089f96d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0aeaf88bd82742aea84da1118a037c00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a972dc8985a349ca989ebfa48fcc4b80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8f1a5a867ef46cb89384c4b144572fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d90f1b6009954c48b92d19f8b7e2b72c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86149902740a44259805509544a9ce8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fbda67e3f174f82898585ec1f16d822",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8c1d9dd94714999af37733c4f6530fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "417140f3dc4d427e9adb71a23fe0c5a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e3839c01605452dace114fec505f15b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c69f20a813c471f858dde9131a010e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d66bcd402c94c4c9845d50e1b6b9a12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "daa405b90f7d434d88281acb8a29e81c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "124e7612a004481ab3e43d03675e7a45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cde477651544de8bc5eef65f568703f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b99974f6c6e483f94076241781720a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "681c56856dab424cb246d6d63ea679b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2ea3c48760b4e7692cb39af719d4340",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afa9e78a9d0049cb83f5f21ca94ca6df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fda2e616fca4301a5f593af4d13f8a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f218cf26c1a4719beb0a2b2f76be7cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63a02fb0e66f4a36b6e36144f86afc0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b08a15e74b6149909a862d99fefce3ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ae83fffafb543e9a24e77260d75ea6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3921fccd0cab4483b574e84f032255f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fafa9afa33b405c8802a90959b1e23d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "295da84d8675459088a4a29b529a10e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acf5ed8e09184241912c63d1712ff7aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89d0fd64380f40bbae119f503704eebd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6adafcc4c1944ae96323d500949a523",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94e691b66e514198838eaa441b1e8522",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3608dfb311cc4435bcab01425b2f3aaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48172857462c4df1930209be71a95ba2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b27d6d876aaa461ba525c2354a41d5ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94bd2bf700e5421e9f9a0978c3f88993",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f83c494de1ab417697b945aaaf7c0805",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a460c7feb693444f8be6b1bbd271941d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d208a2b55f442aa890d730bfe884cd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0dbfa16e09084a56a74a38c4c3da746a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f89810430ef74b9e9dd83001cd7a14b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "baed7fb2be7e46eaa44eb879d12a0183",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a87b9bb7f6d14e6aa7df5a9d738d24e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6a2b73b938a47fc851857d7f1cbec8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f63e05b520d4d0c8208dcfbce18bc00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "539d1bc716634be0bec4c2ebcf87cc79",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a1b3b6ddb9241baa68739284643300b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fd901b95acb4e25b670ba7f5f271bdf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e7067d07fb1416697f399e2b45e7a6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d52a6123dc3241afbedb211a85b91a4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "815a5031366647de8b9fe2253731b311",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65a57f208f3749799d6bdb1ae03eaad6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0689839f68b6484ba56ce5f633a5f717",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "569e03e5ebe04d04abe3eb74e9da9308",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a6c69a0d25c43d28abefac4f85ab061",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e871de02770a47ee9ae005a8ae79d9fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "249eec254e5e4468b2e83bf20fa8a9d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2fca2d25f0b4b5780d407e2857e2037",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3c07408568c405196f289eed49a12f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bfd0c5fe25248e9b92d66d1e59d6ea9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b460c19851a4507b920483510ef2794",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "624e68b1a1894e30ae7dd322e81e8e2d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae2f818a342b49858376d1c7158e49a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1acc4997b0f74a85adea4bcd456629b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f094ff67bdb47a5a962acf6ffadc1bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7eff804fddfe4ddebcd685238726cf35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d02517707ef4ca297cc51b25d81d0a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31e3a71cad794a29b551d9d8d4f68cca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67a3d6f9e90d4dce9d86377111446b12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1949491886c46c19e2106400916ae74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a45f738b20cb451887aeee65a23ffd07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0aae1b0b7094fc291692661cb0fa6f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "911bef39e03b45d5ae693840bb9da264",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43542b8fd0a44070b3611376bb79cec2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8374fa00fa3748cba3490e43020e0625",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf21fb55d1084e4288bdd59cec6a2eed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25b9a4169e3649d4a41924e5137e5656",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6123bc9c26e44f29c1a0fcf46665a17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e76854c9e1b14525994485f2a68cb0c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3785e126923943c29a78809034210ad9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "352ce491a0064fa9ae158dd9b1ea8b0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07e04698545f4c6799911ba52833e0c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f3b007cfb574d3aac7d4fd1b0ae549a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53bbf6f1bd054e4e9d28bcb01758add4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "debf5b49bff646b69fabc76461db5d60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e93db75c1b7d46b58a65386839bf46ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "403d7c5c1e2c4ca994f7b6004551f9ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53195ec8904545d6973bdec57781e006",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93905cdf8ae04a45a1e870e9591d606a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14adc93deb084874af36c23d1a7f27b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b984f201ae0b49019da687bb671043a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e29e8b4c76342e9be75e1ea4c83a807",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c6db9c3915d4f46af05c09507eb3e48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18cb43bb3eb340b5b61a2cc73263e44c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "518860fa8f24464482b167f618107f4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f4a98ea19304cfbbfb063769c19df30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "880033eae3ca4198a26ccdcd7af55f3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13ecbef3b9a449178ba8f5ade860b14a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28a6fee9881444df8cf05052e9c83d1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "348bd730b8554698a8c3e17fb69fd74c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fed4c4d2e3a45b3904cd05da32b34f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4fdec262a96425890fc16db3547e21d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8acf989a42f44d52b71ff56d0de536a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f491b0b81ec24da58ea476ae5e8a868a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a61a512e051e44a49fb3db77601c96ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc3f7de2ce194f908e64a22464d2027a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7645ffea3c94107b4373b5dfd319128",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "738c97edacce442f9d5e1cca0ec8bb95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "name": "stderr",
@@ -452,13 +28471,6 @@
      "text": [
       "`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 999: 100%|██████████| 1/1 [00:00<00:00,  6.91it/s, v_num=19, val_loss=9.22e-7, bound_cond1_loss=1.95e-15, bound_cond2_loss=0.000, bound_cond3_loss=2.1e-15, bound_cond4_loss=0.000, time_cond_loss=0.000, phys_cond_loss=8.58e-7, train_loss=8.58e-7]\n"
-     ]
     }
    ],
    "source": [
@@ -483,7 +28495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 8,
    "id": "019767e5",
    "metadata": {},
    "outputs": [
@@ -508,7 +28520,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x600 with 6 Axes>"
       ]
@@ -518,7 +28530,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x600 with 6 Axes>"
       ]
@@ -528,13 +28540,9 @@
     }
    ],
    "source": [
-    "#plotter = Plotter()\n",
-    "\n",
     "# plotting at fixed time t = 0.0\n",
     "print('Plotting at t=0')\n",
-    "#plotter.plot(pinn, fixed_variables={'t': 0.0})\n",
     "fixed_variables={'t': 0.0}\n",
-    "method='contourf'\n",
     "pts = pinn.problem.spatial_domain.sample(256, 'grid', variables=['x','y'])\n",
     "grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n",
     "fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))\n",
@@ -652,7 +28660,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/tutorial3/tutorial.py b/tutorials/tutorial3/tutorial.py
index 443283b..0de61f9 100644
--- a/tutorials/tutorial3/tutorial.py
+++ b/tutorials/tutorial3/tutorial.py
@@ -9,7 +9,7 @@
 # 
 # First of all, some useful imports.
 
-# In[12]:
+# In[1]:
 
 
 ## routine needed to run the notebook on Google Colab
@@ -50,7 +50,7 @@ from pina import Condition, LabelTensor
 
 # Now, the wave problem is written in PINA code as a class, inheriting from `SpatialProblem` and `TimeDependentProblem` since we deal with spatial, and time dependent variables. The equations are written as `conditions` that should be satisfied in the corresponding domains. `truth_solution` is the exact solution which will be compared with the predicted one.
 
-# In[13]:
+# In[2]:
 
 
 class Wave(TimeDependentProblem, SpatialProblem):
@@ -96,7 +96,7 @@ problem = Wave()
 # 
 # where $NN$ is the neural net output. This neural network takes as input the coordinates (in this case $x$, $y$ and $t$) and provides the unknown field $u$. By construction, it is zero on the boundaries. The residuals of the equations are evaluated at several sampling points (which the user can manipulate using the method `discretise_domain`) and the loss minimized by the neural network is the sum of the residuals.
 
-# In[14]:
+# In[3]:
 
 
 class HardMLP(torch.nn.Module):
@@ -120,7 +120,7 @@ class HardMLP(torch.nn.Module):
 
 # In this tutorial, the neural network is trained for 1000 epochs with a learning rate of 0.001 (default in `PINN`). Training takes approximately 3 minutes.
 
-# In[15]:
+# In[4]:
 
 
 # generate the data
@@ -136,16 +136,13 @@ trainer.train()
 
 # Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the `Plotter` class of **PINA**.
 
-# In[16]:
+# In[5]:
 
 
-#plotter = Plotter()
-
+method='contourf'
 # plotting at fixed time t = 0.0
 print('Plotting at t=0')
-#plotter.plot(pinn, fixed_variables={'t': 0.0})
 fixed_variables={'t': 0.0}
-method='contourf'
 pts = pinn.problem.spatial_domain.sample(256, 'grid', variables=['x','y'])
 grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]
 fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))
@@ -228,7 +225,7 @@ ax[2].title.set_text('Residual')
 # 
 # Let us build the network first
 
-# In[17]:
+# In[6]:
 
 
 class HardMLPtime(torch.nn.Module):
@@ -251,7 +248,7 @@ class HardMLPtime(torch.nn.Module):
 
 # Now let's train with the same configuration as thre previous test
 
-# In[18]:
+# In[7]:
 
 
 # generate the data
@@ -267,16 +264,12 @@ trainer.train()
 
 # We can clearly see that the loss is way lower now. Let's plot the results
 
-# In[19]:
+# In[8]:
 
 
-#plotter = Plotter()
-
 # plotting at fixed time t = 0.0
 print('Plotting at t=0')
-#plotter.plot(pinn, fixed_variables={'t': 0.0})
 fixed_variables={'t': 0.0}
-method='contourf'
 pts = pinn.problem.spatial_domain.sample(256, 'grid', variables=['x','y'])
 grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]
 fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))
diff --git a/tutorials/tutorial4/tutorial.ipynb b/tutorials/tutorial4/tutorial.ipynb
index 505c59c..b070796 100644
--- a/tutorials/tutorial4/tutorial.ipynb
+++ b/tutorials/tutorial4/tutorial.ipynb
@@ -31,16 +31,7 @@
    "execution_count": 1,
    "id": "5ae7c0e8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/matte_b/PINA/pina/solvers/__init__.py: DeprecationWarning: 'pina.solvers' is deprecated and will be removed in future versions. Please use 'pina.solver' instead.\n",
-      "/home/matte_b/PINA/pina/model/layers/__init__.py: DeprecationWarning: 'pina.model.layers' is deprecated and will be removed in future versions. Please use 'pina.model.block' instead.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "## routine needed to run the notebook on Google Colab\n",
     "try:\n",
@@ -55,10 +46,10 @@
     "import matplotlib.pyplot as plt \n",
     "plt.style.use('tableau-colorblind10')\n",
     "from pina.problem import AbstractProblem\n",
-    "from pina.solvers import SupervisedSolver\n",
+    "from pina.solver import SupervisedSolver\n",
     "from pina.trainer import Trainer\n",
     "from pina import Condition, LabelTensor\n",
-    "from pina.model.layers import ContinuousConvBlock \n",
+    "from pina.model.block import ContinuousConvBlock \n",
     "import torchvision # for MNIST dataset\n",
     "from pina.model import FeedForward # for building AE and MNIST classification"
    ]
@@ -224,7 +215,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/matte_b/.local/lib/python3.12/site-packages/torch/functional.py: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3595.)\n"
+      "/var/data/python/lib/python3.12/site-packages/torch/functional.py: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:3637.)\n"
      ]
     }
    ],
@@ -524,7 +515,7 @@
       "batch [550/750] loss[0.036]\n",
       "batch [600/750] loss[0.040]\n",
       "batch [650/750] loss[0.028]\n",
-      "batch [700/750] loss[0.040]\n",
+      "batch [700/750] loss[0.041]\n",
       "batch [750/750] loss[0.040]\n"
      ]
     }
@@ -579,7 +570,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy of the network on the 1000 test images: 92.733%\n"
+      "Accuracy of the network on the 1000 test images: 92.683%\n"
      ]
     }
    ],
@@ -626,7 +617,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -799,11 +790,2132 @@
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 149: 100%|██████████| 1/1 [00:00<00:00,  1.06it/s, v_num=18, data_loss=0.0341, val_loss=0.0341, train_loss=0.0341]"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2906d21242d84dc8889125249c8e688f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5927786adad94552bf6d175f05b40bad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38b7718e526a442daaf1186f02e81e86",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb615467df7b48f386f9cd02ce62fb2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c22d95d4406d413c91f0afd3957966e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9cee448668514e769d67c1d3f44bc9e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7820a54293ed4ca882c247fcd0a455ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62bcc57603e24ad593b1339ec75fcba0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "755b2354d30949c6adf31e78df61fbe5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93b2ae8a96b448529b0a78d3d3a6f8c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8f777bacba84c09956ed8b6a609e2ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd52d6cde57d4e9d811b7d977abbad40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4690fe4571f1484b914937d427565925",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "154950bde2e047cdaa61ffac3a1fba82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15d66ae079be47f4a280663bd7702df7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f409f77f7e748d386f0171bc9280069",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bdfeab263b340639ab335fab0e66fee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce2c004540ff42ee86a6196e25059b0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4bed01f866044993ae4c4e8643e69fc8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba361cb554e840eead2170b8b14c2b63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f26cb10fdf4d48dc869fe294ab4c5ff9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb3eb0e9bf5842e0be25ea0b389a5e97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d84c0e5c3c32481da91ca95f53cdcb4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8675c067fbcf4ee4ac277fbe5320ff77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "682955580a274a7db93aeeb63ef9c63d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df59d7bee02441fbb6e78a77f0d7c0e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2ee8d5356494446b213ac831d918026",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4a93fed5ed943c69e730e153974e754",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bcbd8d8f6134239929b9735ebe609a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd8eac3161dd43aaa6a3551ee3b7e695",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a35ca52a49dc44689d5802e48d7adb85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52a38cc7971947d88a824b2119c84770",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1b2a24fa04844e6a7b6ef9478c80f3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0bc5f27ff354f249bf5956850c878ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4af491849b5b4c7e96f44466f455d95a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "285b1e45f43e47acacfb7fdbf2275e31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3a1c64415344d05bc465d947b16c2c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9458bc3c4cb64876839b908d145b6914",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5052e2e20ab842a7b89c29abc71e021a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5f3ca27a30243a694b6902781b9eb68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbfba59209354a3a808857bb23a23f8c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e650064e53d449ba964f3d67a6b18805",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a2ea116eb8249b3adfaebd1a41632e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5815c3d0921409c9b9d534db9c026b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40dee662cb9c4e229d524a0498b28e8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4f1dbac0893463cbf94fb98a7c3b15f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1652ea105ba49cd82728511d074756e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "370fdf8b0b2d4e3cb31bc2f7b6ada3d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b09182047b3849c88e54d5e2e79d8d0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37721c65a84943e59d41f6778a71bdeb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c2a305b29a04464aafae2956f59da4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd51f25f867a4b8a91190325c8948c22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e096a190c3a94a6891d90018db139cec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adff4e7b2ef84c2d88a8b0e2d525e88e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6c0c246fa9a42ee81fc1b736861e93e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abbfe271a54d4535a912a36551b12cb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f9e9942c0f645b19f333b1005c8c225",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a454023f5e2410b9de9d6fd32f0e35b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04c03a58e11d4eeeaed2fcb32b81bd88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11e2a46c79774b138b1787a885b5e83b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd061a0023114b15a7cea5e3d10c6a42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9b415cce4914bd0878d951e53da199e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b4d304054f5402d9a034f5bd671c7e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cdfbb2c98fd44c54959daef9df384614",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5427e099bdd34c04adee9fe430a42b97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b3ec7d43ca547e58850964c7616e29a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbc8554a0b384f60baee6896b401bc1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5aaa4c79323409e80b594e5b0d6da37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a2ffdde84084ff292a5f8a6b7bece25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53206393c4a543fdb7e6bb8c436a30af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f947126fc1fd44a2853eb1f32341df7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e98f51eeca98450286f18d33bedefe1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34ea4a4bbfba4c77a0b60a0b13354029",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9368d5d6f999471d972d3fb2c0f7e0f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "848c1b77865b45a68abe7c38aef69d9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6a3bd3d05f0447e96592e9091424706",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c5370a1265e4da3940408828198bf92",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6dbcbcf78fba4a7f9b9c53fa14309168",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a74ec2169c644c1da02de37660c355cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cc104a4effc4490b42d12832c022bf7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4759d715601e4f88b918ebe3dff1d5f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "caa917eb19774d90b7d81b81dc24183e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "767e7b1dd3b649598011ac197fc58eae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7560ad0dee544eb6bdbfafff162a108e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "491f24bd3a234eb189d089572910911e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58b86244d97e4458a9f5d8dfd0180c5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d51528c0d3d8488baaca80b249897102",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "267b84e642f94e7f8376bc2918655546",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e31d3aaa1d74c9a965bec39f2c32125",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "baaef66531a444509781e7741b274921",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef37fe7533174560b9ad97ce886c472c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3dbb75604944b9087ac5748db64f0d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c5ea8e8691345df9fbb46a769818e42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e291c66fb3344f3992ae906075bc004",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "92e36e1577d74e449296387cc92b1263",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96cb4c3d948f4b07b06c79ca1b7a1e40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ee2f67be4c34243bb849260636be2c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3be133ba7a4e48899191c541fc8e46bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "092c383abd25422884ec6fd27ec050e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1806b30d90294a0fb0062d0a41f9ddf3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0144b3b085434621bdf7c201b16e0a8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20ea50fa54d545f5925018635e1d2529",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a2af4d0216447c4a6f60a3a40fbd693",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebf7f9eb04fb445baf6d0ab5455079b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a72ddd4824834a8785bb34f3c88d5083",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0047a660cfc4567acb0f33d2c0626c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b43b431d26cf451bbef96a9caf52b7fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fac03595ce314ba1aea83666663bdcaa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1576743347cf4f9fa8b26c270484eb6c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89cdfddda4c440e48d7e413fb2c007be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a90eebb83834f5aa55208546ba7021b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e600c36f452d4a93928616eadfd2ae24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b106d3e9a3874ff6aef9b5195c3bd7ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "703dad03c624458db5b9305e769ce118",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6829309cb6b44f71807d724f2d66e8de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ed1fb71f7304a69a747882d65f3e152",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4407d5583c494044a9df904c7b2ff3e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3403c3be1774f42ab4d04f04e50aaa8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2289a3bf25bb4e00a535bdfeb4c0d9a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f83e1b0e5b34f38bbfd891139860bf0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc73e68199b342e19d7a7f2ba5cc2664",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbe3edfd36cd4787b979b7c7ec41c1d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04e8d871f5ad478aa5a79a98d10e058a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba2210448cd7403a951a8a5ad073a255",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fb907a55bbe42c0914bfce165d4c439",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9600b437ea543c2b423c7b1ea0e2ade",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55556e4b44f546a1ac146b23c06b3534",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ae24a8adba9449d84e152e5f530543f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f9ed95d7f284518b7581ca7604957c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5dc4dd4b8a9449b1a4b4a275b06d54a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "065dcaf5323b42b29fb41ecb9be04ecc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8612e0c9fe8b4af988268bb238cad3e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b2d019b5fa04c66aa24742154035d2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdb279924d6e499a92d6179e21ee6cdd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ecac8f7e943b4d308499f0a9b7e4b0fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7501f02cf21e4198b3bb13d21102b31f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7fd4a3fdfb5485a9d78910f2bfc3ac3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59896903f7234598abce262defba95cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8405b8b0d4c43f18caef1832f45d321",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "914e3e8c4f5143c591597b80b1d17e73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56b2716e06534554992c87ab81d46201",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad9afcaf0a6c4b428c74b55434e06ff2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b8cc251b1e546ef8408d5bf39a08653",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81570ac76bdd41638772b83194e940e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13e827ff40b44b82a9ef255798fbd059",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f83b68588b044f1b5f270e8a85e8ca9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e1a9e22901747babcff28ddceeb871d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33395a52db784a788ce76cb72e8bb4a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "330ffb88461f4973a1d88e1f2e03188c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e04d2e17a79e4d1294232bec21d90494",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d5b14b982c84078b148cfc520f5a175",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "325a7e7675f64ee8913cb146821b7a95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "name": "stderr",
@@ -811,13 +2923,6 @@
      "text": [
       "`Trainer.fit` stopped: `max_epochs=150` reached.\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 149: 100%|██████████| 1/1 [00:00<00:00,  1.05it/s, v_num=18, data_loss=0.0341, val_loss=0.0341, train_loss=0.0341]\n"
-     ]
     }
    ],
    "source": [
@@ -937,7 +3042,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x300 with 4 Axes>"
       ]
@@ -1018,7 +3123,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x300 with 4 Axes>"
       ]
@@ -1099,7 +3204,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/tutorial4/tutorial.py b/tutorials/tutorial4/tutorial.py
index ee1e7aa..90a25eb 100644
--- a/tutorials/tutorial4/tutorial.py
+++ b/tutorials/tutorial4/tutorial.py
@@ -25,10 +25,10 @@ import torch
 import matplotlib.pyplot as plt 
 plt.style.use('tableau-colorblind10')
 from pina.problem import AbstractProblem
-from pina.solvers import SupervisedSolver
+from pina.solver import SupervisedSolver
 from pina.trainer import Trainer
 from pina import Condition, LabelTensor
-from pina.model.layers import ContinuousConvBlock 
+from pina.model.block import ContinuousConvBlock 
 import torchvision # for MNIST dataset
 from pina.model import FeedForward # for building AE and MNIST classification
 
diff --git a/tutorials/tutorial9/tutorial.ipynb b/tutorials/tutorial9/tutorial.ipynb
index 3f19a98..27cde3a 100644
--- a/tutorials/tutorial9/tutorial.ipynb
+++ b/tutorials/tutorial9/tutorial.ipynb
@@ -193,11 +193,70032 @@
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 18.31it/s, v_num=7, val_loss=0.0333, phys_cond_loss=0.0268, train_loss=0.0268]"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "086887ee60e541bab774d3581b7ba842",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe00770124c04181a422b7ab4e6ea648",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9c93d619381400fa1389026e128208d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "181aafb7236c4c1ba5ffad8f77d6c23d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "454e3204f18648a797dfb78957ed9123",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42a093bbeb6e4efcbe724dac693238a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3cb4f17aa2284e428c23b7783942b037",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f54effd32794361b272dc1ccd56ec1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4377c7369c846f88f69ed2fc0295941",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71b7d1f9e98e489a96a053a753e96b19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fb55a741e9d429cba373cd44102b063",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15bbded2a6104ec29f57df7a8fae4c16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3700fbd0531a40699ca95eec136646f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97e362ead1f14920b0add20d8f5a9e12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e809a1d7fb5e4f31b255c33816bf04e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eece86947bf942259f066a948dfe1152",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cb7eb4542cc4548bbe2d56053033bdb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "799a69979e724717be5c5271c1f414a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71c1381ea82645d58c7859a55670b314",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f92cea9037e34cf4aec9f6569ee9dce9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "836366b9629246a3b554baffb189302f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd0a7f77ee244ae0a00191ffdd08c7c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdda7aa82fa74904abbf280d73e26b40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef3297a66dbb4ea9b4426afce5e32d1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "402eb1ffa3ed4ebf9f8f1039d686d25f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb711d597f114ee79a41f2aa1c944a94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fa745ca1c0c4386ba4e0508824d6bc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4dd644437fbe4e898986970900365d6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df58910b95d04ec493e14246ef90e823",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0548aa2410f44937bf0a2f61199369c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "862300ada2c1499696dda56189535cf0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee2931cb3a45467a82f18b0c5831d12e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78410d81f6d74db7ab3a601d59e3026a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "324d866df58c4a20bcccd3436dd9e9c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19f4e516c0f543ba98491694a9e0087c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "183ab728ecbd42b3a72754c575184f30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "920ce2873e7f4dba957ebfc70fa4dbfa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d67029ddd6f3491a92be6d6ffbca9a12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d64c3e21f37472585435b878dc37b06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "764a0c72560746f5bbef3da714537e3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63faa5e5d38d409fa0e4521e31f58c95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b4fb5d477784b91b1f411b7b4085fee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6be5a98ea96c4149a47a33cd53a4f853",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76d53d2842f14cda8cf788b2bb8b5e01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7026d2821ef74033ae1f1360695b8774",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49cce0dbdc2243228b68b9e2dc8b3738",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f381e54eba824669ab9fe37e8d684585",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7dd2a99315b451cbc8feb213caf4f45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b02623854f084153a3357258a431f997",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d27124fee8e943259184ce7c1553df12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ddb765232801419ca058484ea17d2b1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "702c4e6db99f4a998370f0a8bd578dfe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9080b9bf34e74af1ae8c86c210de7e0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f2b5bed6d714aed85e33187f7fbb2df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7d4503205ba4362810659e62adaf621",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9ac730504ed484c8e530da0be992fd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6291f499d6854cba844bbe92adf8b614",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d775eba9b20f4ba08aaa4b8d689ad916",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "060bf05f24294ee2ac748ea3fabf41d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "44f762d2ac654ea69aa6bcd511b18ccb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39466d2bbd414693a68f170657438584",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "708308f1a49d468994ab356aec1eaf18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2138d292818e42498acb7a116c3663d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b6042f475d14c998d102bba4f09c783",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f31e3c53a7d64fa69d10fbfc24c24978",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da4e29d1ef164f378e0449fa09ee02b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b81f4871336344d7895da6bbe856a7a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0902db621a6146bfa2de6b2027f5f68b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99d51b4011e74b278f2c9546c532040a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7ed170a472c43c69904c6ee671146ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40f3dd28fcbb49539fcc6f5b7199aab3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e092437fb6241e0b3211d9e5b79a954",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a3b91e0e10c447fb48d84dc7dbc5533",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e14130550ffc4cf99bfe38a509def93d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f492d346f4a4ef9b32a40ef2a253895",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d35e0f95ede4dcc96419e00eb09151a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cd38752ea9a4ad28c7f087305f56218",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cbee5277d2e401790eea1b585e4cea6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b19274de10a4b5bb77ebb841bd30240",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5486b59d3cb6452ab196d9b49e610b8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53ea21b77c0a4737bbdac0b32d29f887",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9634e3f614eb4b25a9d4f7c87a559450",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89501e9e15b643368649489962c44ca0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6862a8378698413ca8176b4b24f08363",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35209082919b4c2383771de95db5c669",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b96de4c3e814473999a7ecbb8a426f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "636a6851c873442bbd030e760ff18654",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99286dcb47df461e82cc122891e6e1e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49b559f40b1941a397c510ee06ae5987",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "295b5a7a65744ae2a1be07a0acf0e8f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31115852fbab47d5a9b4266b9fc0fa73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e89b95f2d2e49aa805013965b59ed8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "529178a08a76491081ef800366bb62ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "614de5100d404b02912dad237f9e322b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9685b5b691d4347b5fa08bad2bb7251",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c183d7f61703476683acba182ed89111",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8594899842514bca8c4c808aa62f8140",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50e0338e63224aefacce0401d952e179",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca6121583dc94e88ad99295f2235183f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "698dbf3463914e00b2a0a636302dc7d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb8543002a254c9e88b8f752586c20e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c01df06d84f482eb31d26a63715570a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3c37664f5de419cbd0807291b638e18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0595140d6128493f99dcb6ab4ed7a86d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e1ad9bb47f74889b203f3cf07a8827d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10d07bee4d8e4d918463f35503a8cdcd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad4c46a02ad24c7296cb5079d3fbbcd3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "531356cc608c4653b1bbd8000bdb2b80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b250dbd1da754b97b5a6471f2504fffd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64f336715d164efc99d2906e5be53698",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24b72569fa924856af7e7334d4082295",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7455caf11f04fc28b350c08908fa794",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef55862fd2944c47b42a911e5f66af33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42d055e47498455a96e5ab5b264d2787",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da80de4a21df4a91a1dfe9c9da039d53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ac4e2e5d7204bd79eae513fab9ed52f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21f906ca946645e9bb62109f93b2f8cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4cc387d853924abab3fc9dbcb8bff1a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "228a7b769f754c42a28e789409026bd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36b98f54ff074d08851e6af54d5987cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d87a43ae3fc421bae6514bdbfd1f75e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fbf1ab6254c448789415235145b42828",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3c47e8cd3774d97b7d7d2985adf7ad6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61f7e4225af44499b8268d15b807fac8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7457b048e28c44de886e13ef13f940b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db33f44121ec4437acf94507e8320da8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c00fa1a296a644cc944cd4fb1c6dc295",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0aedebadb2bd45eb85a2524c6c633f9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2a239be0dce4f3394a72f19e82342f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5f18c74155347f6b493070d9df05c38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d24e3223aa04a2faf9512ad106c14ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf70f7c0c7b8404d8fe840311cd9e3dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "246268c13ea2435dba2c413adae0bb26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b228d92d4774ae0a7c6ed3e2ea9d38f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "810811b9c24c4643adb768367033134a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7c3ccad91734d02a5fb28483bfac708",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "845fff4f3b9c42f3825c526d982652a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f7c4f34f3b142868ebf4fa93e2adff8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e979638c3835412c92ed402f8c9c6076",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "865416699fa64d7db0cfd29c151ec031",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a881fbf08df4e3ab54a373281a1e31a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94a98f9e290f473c873e44ecc8c1324b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce29f40091584d40ac329fc91dc8501f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4eb26b673b8460abda2667c14404901",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cb66154092f4899abcff9b0aa29bccd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bae5e5d04444d6d8f97ea88764b4b11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4860c9072e2c49f2935351122e34084b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4c7ec7b08954abf9142844d1b9dc36a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96179f6836474baf9042433253604828",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d198f6068d8476b9098d2985ff6a076",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db1e972d114841a49e09c97d3d8dfa4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df5ea7282acd4c5ea757d5b5c2948737",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "555cce8f875a4485bfd426c879bfacbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "452da9d26e824fdabf1989c87b6cfd5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f509401765c4c58adf2f09742682578",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0dd951de4a894273bca2d9088b37ac83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aacece66d2c44360aaa7e33318e13a0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "009ca6fbb8c747799963b1c469fc7085",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40585c8601a34bf0add17829c784f887",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f228c9f62efb4595b38a1e789d11d643",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8fd8094c0ae4b61b6f2e3dc0a20e326",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35e59ab0615a48288320a069cb543031",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e8f8695f65e44aaa5b895c67322d30a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b0c0e4ad527440cb49947de687078c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "849b2d2ebacd49aea745a4d70dcda6a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c46ee802a6c34ae4880aad222918a8e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37d38b0289234c508b7602147ba62d6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34ecc1dbf50444b5808c0a79a650c6d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b32f74324904c6db06c4ea9f0f7f4f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fa05efb31074748848241ed69f1f5e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1024258312de42aab65f120564f4e54d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1dce01bef40434195b5336310cab030",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9b8d4834a7c4db6967336be9328080b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b1c7b96392048d58fa4096f4868abc5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a53a1d96b9243e5b239239ef109259e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "175aed7d40bc430eab1c4b5f648c9d25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d56f00b8baa14bc9afb057a2a0deacc9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0a640f0aa1748358f8895d85fdb5b04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81f53b5aeafb490784ac3b9c1dbf21ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38b8821b4ba946e594b65d4c60bb761d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a919499806b34c728773eabb6282feec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32f5625b9ef24191a95753be8d98df43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf5740e39862487f9b383ee32eac1694",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "877705b23709403bab1f36ff29735b3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6fad0be32a84491923d6fb359bb4abf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab715ef2648748959772032d1958f970",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54de1238d72b423c9b60ab7a48176a98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "998aef67dda2442f944834bab3f0882c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "360189db065042388c917de1fab398ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc62b01825e646ca83ade60fc23f064d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f81ffa64d204440afc7822ae7844fe1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2367eacb143e4788a2026775fda20202",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "238ba14728db4cd2ba582f8016e86289",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a8281e751f246aa816721d08ebb12b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb2f270a94214aeda57f675928557e17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "416efc855d7b49b5aab272ed4b5d3b64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f80c0fc2941442081d69c53fec0e6d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d59ae41580004641bf6bcf2679697f51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36cacdacbd854b0c8401d98b78a26068",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55cbf2a29b2148ff8618534103a514a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8624a29e387c410fa7bf6fbb03b0cad1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a37749c2625b4396a7f532f6667de130",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "051a666bb3294c4f81aef86cd1f1d942",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46dcc06af3a14f73ac93d1608fb3f536",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59cb278056084c95b2bb11dd16ff3807",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8dc3baa61d89430cb7a69f788114a432",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f5ea8d079d04173b1b3027e0fb8b347",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf9be3a8cf7646539492968927fccb14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f337ec5e829b42428b8d7e0af87b2ccb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a46111b6a748467cbf0b98cebd2fa485",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7435ffb4ab1842ebbf96e81af1fa69a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e96b9fa1de644fa98c771cc6606dfaa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b58b5bdb0c841a799114dca5e1170bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba26667b65a14bf89f6c3f3da24f39de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f98d0d3b26cb45b9a79861b9ae617096",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b753efd859d4d7382768c8d19b522d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "061d2104c9b54c128029ba62d774065e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61a13448906a4bf9bef950cfb0e5f94b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9086eba9ae6b443f9bc86fdeea4dbe0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c60a4ecbeaa4176a5add1b653e1b19a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c79fd6607a9e4d66a1d9379aeb7453aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eaa306804f424fa4a6a15d03856bba5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe6ca9a2cdd2417ba11d95f51c3850c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b321ddc091642d69e14a7be2539a519",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07154ca1df1b4f7ca10aaeb1384fe8d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5572eddf0d8346a09f5bd9f2984d9f72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1dcda5b7363946fd942a4d9c84b87481",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "92fcc78e63d24e09b93014bd61756a62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7f54436254a45c19b56fa20f47e9c61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "518134d60a7a4e398efd9b63fdfde6a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43bb2bd579ff45bb9e5f8a48034fbcec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78592164c9f54597ad1af8a2b917c333",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c75b73ea37642bf8e22eb834bf981d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99a1c0dea03949c5a8bb008a2f596552",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e464921d39c54090b7f2c72fec5f1c50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b729596f93d14e928e95b2b7f34621f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8947aaf4dc1345df99da40b40389d3bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28509c1dbf6b40b69083031e4515c74e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc74bf3b11fa4030a9c373f35d199f4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8734c52d757400f828ffe95c2107fde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5fb043f0f524d17a174110a8d95a088",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3af5725f1d6e44ab9c73a40cc3d1c014",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36b290a7175d4122bfd61e5cf8c4cdec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "517bed7dc3ed4b9fa4bfaacfab7a0fba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a42c4d186c104ebaaf22c67b96810547",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cad8de49731344618e095c2983fbfbfc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9cd44119f1454e46a2d895e13a2d28cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f85b1c4723fc4a3393d46deb3f3f83fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fba90101a9804882a39aa66293efb1df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00b69959b9a540e4bd332f6de3757adf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3707a58b4f714b388f0ec571c8f00526",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e5ad27f2afd466e8296c791d4e406e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a81f549071442e8b2fe77423d9c9334",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1f745bbd8b847d9834df8dd0793d5e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a4a3bacf5bb4820b637cd89aa30ad58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4acb8f7f2b1d45a7aab1820cca51e1ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b91be2b9d04242e5bb37a11acdaeba39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34e6c116620c439ea20a9b78abff662c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf9b2e952ecd4aebb2353ad8c011c56a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "557786ae288e4477be0750a814a7f3d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9dc2fd4ea15149d8b4d243a9f18426d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cebb6b7fb3a4101b4adce5324da1ac7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e4b539465b641e693e5df7946638c97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c701a3d652c64b099e206b0f2294a313",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5369e8aa00fb4c15b652b556fe3ebcad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5477164b97d4ed98dffcd5363e33d93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3a29510f7a1490fb04287fba6ed3493",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "543fabd175f9447ebc20c686f894df63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cfde505349dd49aebc7a5edb77d80a6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98a5b33e79294f3984913559ae1b69f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0521bc1210d439eab57745d5036c56d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89f563cfdbbf4176afc15ca471181ec8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b920968a2ca74cffb9d4a7b7d45629d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0eaec86cbc2456ba7d3524cabec3877",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b81331fbfba446baed9dd1122ed4324",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "336af0c2c30d4aec840a101781622963",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c76b27791dc4bac962e1d5860b7aefa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31d2894c881f41c598469ca61543965e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f851097a4c54ea7aa27e8a02cfe57d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25006a48e1f448b88d7d6008d18c47a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b741ec4fe6e4e50afae0a9aea701ab6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88bedb5d718642458ac3bc51ad4948ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b10c38ec83d47f9af4878e1aefacbc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1904becef91464fae6751798f8587aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1b2182cbb7f4f2990c718983cde9d40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "029a55be0e51406aaed3b888bb5f15fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81b802bb04254efd8b94a56c70c3b844",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19bcd1ba2f584dbbb7f4a59c7a882bb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "483c00962991437baf0cb27569274584",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f599369c89446bea494147ba3d55ce7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48cdb4d39e0844e8b59c55c2037f4c65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "414df9855430414ea9e104f98da80e24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21c6e57bfc2440db9518d793741cb7ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fbe603764d449faa3cd03848b27c8bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4aeb2bdd93f34556b02acf87e4016be3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48756920229c4ad7802be0873ca82650",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf0190530b0d4774b119d63b7e343526",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d123707337542938bafff82aa35843f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6baf969b22e740d5b3c5012b83b8dba2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b7b0f8c3e9a419d8db385b414f7634a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72e30a2b8ded48a6b4c17a3e6ea54145",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f7502bc8ef3454e81f9a1e0760f41eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80cf8216771341d5a7d804c435bd7152",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81f5cc8865184af19f3ca63e3bc13b35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e30d53c510d4f28a9243fca8c62b165",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93d1b35650a442a2bbbfdd6624a5b04b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b16ddcc8e4ff42c09dd4a362eabdc117",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a697ab2d45647ffb9b13c039b673dc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "44d2ea9f413246008580e19ceaf82fae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d551b379dd34be49a8d1b3add5a9698",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23794beb966a4d7b8ac385276a0c8292",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14a6fd9c73a0498591c688f9d7f0a315",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53b1ab928ef34b03b63da427fa9b6f9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7084a43340a14a7cb71e679f64e8e316",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea64355f19d5441f814cfa5c7bcb259b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63396e6daa4f46988d3227ce4c7e6d82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4c008d3535b474cabda1e4292e8b96f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23de8465e35f4a5ab6386f8902d12f2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e244c364cbee4067b2a21a3b2bf4fcf4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d875e7cd780940d1a5b440eaa820041c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0eb24f5ac9c7435fb03c9984e8a75cf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3792c856690a48328b47b122c616da16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1019be42a48b49b3b7b6e12fe9d256c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8af4182fa84740f5925e2606b9108537",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4aa011a6047941b390c26d3d407e7d7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96ba9e994de24d439389291bca37de2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e265686596c244799a29bafbef73374d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11268f7bfdeb4278b5437312243357a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe4f2b9f70dd41268991953a85fcd56e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5b5639a54d44dcc8c4ee0644de2081e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fca13c4dd44642fb82fe06ace99a5760",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a059085a031d41b98f2a70c212f5b812",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b54e64abd0b4e55a0ff4edb29b0f4f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b36c2b029ba4f5ab2894d760592b573",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf8a5da4db964139bd9bc6ad0fa61d43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c5ecfeae2344eb6972808b1982b3a4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6471a303314e411c9e5a4af4e5237aff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce73347fb0db421d88054525ae36b0fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6c05ad041ed46e0ab19ca346b68280e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16ce0f5dab344c0c807fe2b0a17f1641",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74c06e62548a485a9634171598e4f5f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1f3da668e7a4b629108d63927dfdc03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b0b119d995c4519ae6d6624d8e0ec11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "790bc04e8c574daea2b865bd995b84e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ece619957c90482b9b6318750404c0ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1e3a85d716a4ef09c198d28c82d18d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f55c64276ac468b81da684ad8df5f0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7a21a2930ac49a291db3108930ca687",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "739ecd9380b74a6fb12f6481889ab9a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e4d515a699849319d8a773aad79de8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24caf6ebac6048849bf5c298211b6009",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa6be4c978dd4e00b632004ad8102b11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e908469580e6416593f4970c26bb5d2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1d712cf1c66415ca9f60b4b405730ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a890c39eb6c469295f9e847bc9c93e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c459d9ab98943b4bca77611734adca6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ff236ac6cf04f0293b8dc61af0e113b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4b110c620c744fb9e10701e80906ba7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8925cc23ac3448fba825ee998805e6f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14c3dab6a0ea4a038b431ea319103681",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e729eaa7a694c06b50162050f87bd5f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ea822ae197241ceaf7d68ea42f1428c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83e38a875c414bb28766854403a59f93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "740546027a6e494f993fefc487282160",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5a48a494dda4d5e899982787313070d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eaf3416e165142ecb7b774a24b22b80e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd47341b6fb34c888a2a23d9789529d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce2fc415548245bb907aa528ebea3fe0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "823906baeb1348bcb3f8fae8e4955652",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61cea9672b354068b06c93493df7f054",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb6d69fcf1c540e08d038197ad4f902e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "57b69ff788be47ecbf6b88155e8e4936",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c482eb0c6d54b609f732e0689267d8c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ea0414f20304ea4aeb8b84700642b55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d528d9d20bf043afb87d726e81a5670a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "359fd0ad3d0e453e9faa18e8a4da8bca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58bdab33673e428e9f7ed4c904f57f27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3a879cff186466fabc0fea80c071680",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "310a33a578bc4a8bbe07c9916c0759c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e07494e0253a4645aad15de1420a91b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f128f88e4c99425d99c61c22b7d7d6e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b07e5cafd754e318dadac16f4d68346",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c51b86f40f204768b9435725738f6b74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd22ce2f84b545b7adbbdb4fdce441a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cff5edd78d6a4f58ad16d47f1e196dea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c5bd36f5bb1484b8edadd112c1c23de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90e49784622a47a591c3a43d70df7583",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a93e08b38104d5d8fc4002b3fe7028d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6df2151cc8ea422e9815b7fe1455ec1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "515cc45652ce42eba87cb11068dbbdcb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78bf1f76e5ad4de5a81602dc6acbabc4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e60826a4f9cc43d78028d34ecb795881",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19dff2aa989f4d9db2fb5fa5e9d46af0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edb92337133648e2b1ebb5beff169880",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6754c12ef7104e10844eebd1738264ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38f43dbdb682411e8fb9a806e398c1cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aaac0dd6c0e14abb942e0befda40b926",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98b8c64c64c34e21bbe2739ad45d0150",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4392caf13ad84848884093722fca576d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "977fe41496ec4d10842bb4ab9204fc4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "970e613ae2f84cf78cb1c5c2dd39c6d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af42b89a3687474c86aff3c4a2acae86",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe3cc96c79384c2da2fdf5f7a7830bc4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ee2943b86604f51855fe505d2782edc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d749f23bb3340d5bd05350d6bbcb028",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51d9df9f290e4595a132ae82b1a094b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f8e71d1cc34489080edf9becc35b02f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a840d54f6774e0aa4d3d107cc35970f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5e923078e9e4e7a9994539ea597ff2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d489fcc9c27b4dd1a0259c5bb3353201",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88c2c5430b5c46428fb74cbf4933be9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a072db9c67e44ef9b717ccdda8f0740",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55b6aa5a354d4254b5c3db8914c64791",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef07a59f1006412c8acc406afbe3cb64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "046b554d61f94d7b82f0a1e40d9b9807",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f28db56c7c844e6b3b8a820e6285839",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17c0a0579b0a4027b86fb17b07bddee1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27edd61b0f744d628a0edc348c23d0f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b32c8fca9f7d4f70a3d2f3d4d477861b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23d9d1434b584f278704dcc2dfa71614",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b57673c9ffa242de982f0b770cb51fed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4dccf0bba19a4498b7b3fd29f7692153",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3546964867204a62885a3264021a856d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40be5a9314a2477a8a02d668903a7d03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fc976bce32c468793ec72e214dbd87e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "623bea5ceb1141a8b35a934a6f805d75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60a65b534d5041a882d2bd630b45ebcf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99335662eeff46d89b4b41eca1153970",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "433891903740464391a4a774f80bc45e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a45a29b7fdf4fddb0b9515ce875270e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e205039088c140dabb291d03c3c80cbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d156234c3cc4cb68791cbb0fefa0cec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5362cb75a1c74444906a430ff3b09a07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "872bcb73b58c46b7a65ff67fede8f5fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "139424acfbbc41199e03822ed06f6b4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "744f1f6361f2468f99b0e2fe76dac55a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca90f4485ac8435fa90e636180a8ac57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74f5b9d567e94139b8a349bc5efd7d3a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "227c4c53b52d4c9fa2f627c3bb959d04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f6247581fe14483ab48df49b5e810f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "765fd6d4cbbd4a8e8780c8ac97c910cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86f1a2aa2f6341df80b00fca5efe4050",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5199af4656634b949b7235f462eb6daa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6588da567b384de09704b03b803829c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f78d9d658d64398a90f1d4de32cb038",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc60aeeffa314dc0aaeee839f93bb3d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a194153740c34f098b335677dd66f640",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e696c173d43540af9d6782f04d385ad7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11c98580347149a7b706302638dccfa3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76241eab76784a67be2a12e790c9c065",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "998ca4b7a3b94f0fa9c7adcca6ea7ce7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2372f0da6834b96947d49c9787f9432",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6967c5675572494591609bb94a058f5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adb518bd7c184f5498d4d099b89e2737",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f00d7624af546d1bfb6975d7902221a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcf6768bef5541b6bebc9b9a0bf31eda",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16b0d1ccc590478a8ec983680acca601",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "026caad39a8242f1ac96a01babec9768",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69cc3942186e4162b346b1b939f1ff7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2d725c8aee54694a700f05ff0459f8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ec22d1258f0438ea52b736ac9d09cb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1aed12703224b21968e2ce6cd392da3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10f9ba387e344efcb43e162b3f857f03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1038367e31ba46fd968bb98a57a140b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e33b0592a2d4418ea9ed09bade695385",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65947fb8390b459c9e1fe350a271a1a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91a780f67d5d4c84a4ae7bbb70af7254",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0376ca3cb1eb45b2ae66ee2a3a1272e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c051904092694c12911f88abd6d0a1dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "738b8b85583f4bee853f1c6113d43914",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "301530e55be94650b7a097daeb710fc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a065bdaebdb42a8a32529e30942ea37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b55e0ade47a146dea03d81a7455dcb01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36de65fbf03447188541d1df9fb72954",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "039cf011deaf4e73a7f952e309cc071e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a3d5f0280f84e3587569fa2b3b35e7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7562362b528946ebaa2f9948ef3c0e2d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e13c7d9677464f5bb30569a84af9fabc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82772c548e3842b2b67b387e7b5b50eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12c0476ec94d4ac59ae5e7e4764de723",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aedec52c64f342aa9a95005598b207b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6b51e2f5ef14218a0349a0c824cafaa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d33d636410c24e17b0d178ba4ae87876",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dada4f231e544ca08cf04f35c38600e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5448f3baf71446bb19f893d60e5681c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b146afdf1fbf46e98f282fed3115b55e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c33a569b44241c589d33fd8a4559882",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9cafcf87fbfc407481a335894d87a90f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a579e49c1ff4446873e20ed6e726a65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c55d059c6dc14633a2e5f0e6d34b4edf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98b8e086c7d74d9d91fdb081b882e474",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49e13a01fb5a45c8b54889ad1a42df85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4bd56483466248cb9bfbbaad8da8dbfc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bfbc1bec13d4e6aa606ed1347132b53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce97369e61bd46879fc92ac618b788a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11e70fc7ff344da49fa3760d766c81e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c526d4edd8845df956550b7c594dc0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec43718f4a0940f98db6e5c90efc954d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d02d636ed9f446490e7150d8d9c9dec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5291a5ce9cd849a2b6ebc74ff225a011",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbd31a4a69e54c9fac745131af8b3b9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66a7d04cfdc74ea3849567844f47bf35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f657d3b107fa4a0ba933c1fe9897d583",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "182e7ac4c1464dffb59d9431e2acff4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71741e5e8d6444439f351502ec3caea1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f171d184dd4645ecaf04b8dc20ba71ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5f9f2a56ec74f68b4620908f4ee4ebc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a256df18e5244a01abecd6440f90e38b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e297d3b861f403f82278a89b9e24cec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ce5641ee91544c4b36efa2709d4890e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4148d9b68e04da1acaa5afa8e4582ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f555651c311e4788b5ea27774bf5f70f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bb05a12f32a46da80b280532cb2405f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15d059de80524fddb0c83d5d583a2d3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6fc4f6784c84a348800dd983c8f0c22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "038e8e3d1a7d44e59fbc76da468b7b97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef3513dae9384a5a9b9611eec7278d1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6480ff37f55a44948729ed0deb96cea5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e5f2452973a45f5b5edd21391b8d803",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec6d0c899d6b4bc888b1bad08aa5e37d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e05ab4aba894870bb531b3b28d787c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aeacd228ebee4b4887e275237ddc57e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce92e407975d46feb7ae848cbaedea42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cfad3cb600424743a92989267b5be8d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dce32d1d8dab42c58bc92ec069ba2f29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1bbeaeeab90f4ba6a96b91a6ffc52328",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d463b06d28724e8a8512e1dfdc729681",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd202ef4169c4183ab51f6003652d968",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a73034933953461abba0c42bed73bdbb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fac0d79d65984e8c8de1480caa2bf3ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0833623aee3748b5a45453d6d86925a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97c1884424634c278c1a7f6770ba8408",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f9d12078fb04c7f8884551d46111a64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1158835314bb45d29c5651bb187853ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "057eff6b06ef4e9a9063c6103af4d8e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93b666e188d447749545b67f6b942b62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f120d5f318494d59ae9d0d27595a76fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6234093c711747ef86f1b71bad29d88c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa7b782583bc4bf0a586fcffa243ff01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "612a2e1709ec419a92bef958ab20c31f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ca04a0b1b2846388ce034edc0bcc84d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40119e6c11184d7199fd5eb8e8d4abdd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c35f1313f2554d7ca77ce9e377bc8892",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e77f8b874dd453281cdfa82040a8bd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac8b1444dc5045fb8153b849727a3cfb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "300dad4400c347deb2b46ff05b67ac31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff9eefafda084ab3bad689f1131a82d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75aed9bfe65b4aaba0414040c1b41cf8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da97861b83394f8a9e5fa1226190e851",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c2187359f2c49b7b609ff4956deea24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7678fb719a44781a0d5b5bb0ed508b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51ad057a1f3b410c9a3bd72da414c47a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "275232b7ef35475380708774b1127db3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c1524586d70424f9230ce00c9b0b777",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a4c1018d906423589d59e67b0f3e1c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f05ac10e5ab44e1aa4b2ede1e725d8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb7d5340181e475d95cf9fb93a88273b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c538f4157fb3481094f2966601cadda7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae1aeeb63c864dfb94f155d13e24037b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "260736aedb6e4de38eb55371af63caea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d571cac69cc4405bb17883ee62cd8831",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52624814821b48459d36630dae562635",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c888edeb3c9d4272bdc1597dc7b02436",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f6c69c941a3470fa14531f2cabfcd7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "607b347923c44ae9aeb6d6a5a12546ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7be47a830ec4a18995d57a57ab097d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72e9494745cb4a2d9b6103e07df2896a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f70108eebeb4d6bb85261cc48421483",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a26f8dc65f64cad96bd6cc6abba85b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46cb56f6ba8c4b4d8f4ace8232561b91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1de88b529bb74611a713d877e57632ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e96eda7eefc34c63828a75a4d49af54e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "145f66e9b4844f8baca33a95bbed4e1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c55e2f36520e48748e233b4b22bcac6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1525b52e294848af8dc340a0d61111fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01b24d5a6622488bbc158dc8096a43b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47f2266ecaab4f94854cbe471d5ecedc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9822dfc9e644d0f939aa957e62e977c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b8129837c804ffd8d7390365a165917",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9883c25a3cc74a6883507261036dc2da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5391515f5db4b08a131e7f956a54e26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df50984ed3144b1fb311555e1372af8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffdd60e2e38445268a2ce9a6c0cf397c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f38281414ee5421c8a3936788cd21bdf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe78f9eb641e4fc2943ee1f65368879a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0397cdc01dd64a80b7135fe98db6f8e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84020f9b58a5458e9071dc235916e742",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be942bf2f7204ebcb2aad16b4ac142cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2847fa54e3d466eb7f421cf95d89f06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8655b6bc01df40e69aa44ff84d581ebf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7464870f196b4e5e9ffe27028e1e16fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97a45907203b4bdfb356e16d612f1766",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7628f26e10c415ab2e755a0f1eb9b58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5a6e7be97754cae9ebe29f3f61dec51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "699f026e773b450fb92e655940dda34b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bed21ea8e6ba4d38bab2a4676a3e6229",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a19d986bc7884d12a6a430e74be0fc05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbf99bb79150495fb0a01de39a9494cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "002fe504a3cf4c20979e520deda3b7dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf845c60e5e944838787b7a22101345e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05241614318c40398c14a01f7b7b77d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3161e25715b04466b9c03eab0cc494ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7cc8fc36d5c54ac19e69209e2e04be28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de715789cedd437daedcde286f3e8784",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "520323cb0927407481781e9b65911734",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab7d1d19608f42198628570399fd9b9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a27cdfa40964c16bcb6632db56a858d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e424cf12c5174e5ab99df1e623c40605",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4c2460666cd4b2f956a6d54879fa882",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8538e83ad2448b7a3c2cbd4e8f2ef46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87cde1f30ff44fc8a9320b6d4a1ddd61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46a6094ef32441e6908663917487b1f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54b1fd85e45b4bd79673b6972209f3ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d54de72e60dc4fee92df4ba0bb8e8fcb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46d5609e56704b499bfbe66f41fc8d61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00158ea99b5849a0a0761fa955a87075",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd904b6137574e049a977dbfa34207e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0634308cfdf47f3b76961427f85d397",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "675decf1e4f644c1975eddbefd5a4461",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a53862b83c24cbc9d00a06b30130e40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7bb78c499a54f70825f727ed4336fa1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12be754d7d0c4754915a133253bf1108",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a394308aee0429798641aa8aaced926",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45c015c3ef3a46ec99d8507e5ddcab81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e1a7c9935614a42bc2abe4fa5620b4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6ff9ddb466549b980f3ae2fad38f0a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95e04d07bc2946f89b3909507b0890fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "147465035e404accb95b0940d3071657",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7a46871b34f44b2a3b57939ac74fff1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e28c68251c8342e683317e9e04e974dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ea2a525a8b247809cee5b5ff6c37631",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a601a2a39f914a98a1daa6e025f99e5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a482d1ed33e489096d21de3a8ca0377",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3893d65ee1747d7a7adf66262a06db0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f7b2b5af25f44b4b6b3572928aa4e48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00262d2274464d02ba7e34dccc64a32f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9fbbe6af5644728bd51f611e7258d0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "040c66187a2a4d0993b9f079bd70931c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b16ec9caa75942609f3d3f415319af16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7ba37f976634f17a8d786a86a0f1424",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0885816ab5b14455893fd4d06914f3a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "defcd00cbbce4c95912e24091c2fef44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7b8988f353d4981899f577338cd2213",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d8ade0410b84e00aeaff50e97291590",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29f1508628434b60ada4a72c91dce2ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa6386b6f89e4f3599b82c64d06b319f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c2666e9ea0d48a795b6e10b44208690",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b348971b9044d8e90f6f11f62fca904",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2109afdf034a4d05ae6bcf35e155ae2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc86c0c926fb452a8a1171030e0c6eb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd03273a57ca41d6b3fa2bc42ab31c5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c48695fb53c413ea4ec7d4fbb387f05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b70be899e6a4a94b0da1b9cd5909539",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95b9efd72f824e2a9adfe932d196a55c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e36009419784ec8a051a273f5e0f2e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4c36affce92492ea43e5e271ba731f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34988f7548714a7a9a9b57733eeadd78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02ec5a8e5fbb44009a61965807837a06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21b6ede351ca4f8f85dc03b0dad7469f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f72e90ce8924ac19a57fe1653465c3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d60bc021dbcc45b0b0260a2b26d068b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "354418fa51be44c3ad5133247f92f71e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a12beb488e0248549ccc284e76e21eb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f1b835e1f2f4670a8948dc0f6b0e3c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fb5cb099d4146febc7a3c0b3b6da528",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bb40e159c9d421eacf27b6896eda738",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aeb2ce1827c9410b94277e5be27171f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64b0c10191cd4c6db23f16f65e61d40e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2cf8955acfd4cf7a7c8c579fef3e719",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f57f80c36166451dbf635dbd1340f690",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ea3dae02cba4520b94dcaa69addd140",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "671573f5dd6b44c99bd28ebe1dba627b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c723ce57ad34f9291fc94af7ab12e34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5da871575df4930981ecf899f6a6d78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e1ccae22e374cefb3027c0d64453432",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c7563a46fb54f1ab6536f30855bc547",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8b1e7c3935541f18e578a1263c196a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c20b5aae1c4d4cb48c81fac840a142df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "855ff285d8a649f5aed2978591434154",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8931fad4f28e4d4b9bba32a78f0dfc70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1639d4fba5d64522b47adb72e5b068da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e8cd7947d094af9a54f4852e3c1fc32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9267a0ae772142659b8163f445a8effa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c91fa6808444c42b67c20be0e2a0972",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9622c2c6c03d4e2dbf55c3fc6e9bea24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e6255fae7b44d0f9a291477bb8ec66f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3cd7fd0a6b9b4a2599f40d774cf106b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3016eaf018fb4bc1a142973d300429b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1af2970bf9f74ba9abc8f999e5d35483",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb034c1deca04d20a45272f72c85c27a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f1d0930ff4245bb86d49570641c3029",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cce0e66d31b4b7b8d0dcb4b1b51f9b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd622d913c134349b328f623adeee6ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e5d8021a9ae453c9db6fccbef6b2337",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78a03c2f0344438fb89cd96c99fd0796",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2363a63e272c400788bd44dac0399dae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0538f831818841f68171f63f569f0ac3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd0ce2415f70448393c3f62e6d7d0a42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95a5bee8a4414ef29337eacd33c3fa2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfe4ebb249244fd6b919d36dd153878d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce9d707295d0404a9ed60efd03879961",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71cca4d1848d4182879740995af2796b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a411b4c65c394e21a085fc997b2fbc0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5bb1dfdc8caa4702a62fa0b284ec2883",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8448ec1889714cddaebdf2289027f0bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88d5b60fb00f4714b9b81f0360fbaf2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6ca648288484ab79367a442ea542c6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abc15aa7dcc5473c97cac78f4ed17298",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fabccd4058fb4b928f625a06d8945034",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a5dd4e67ee940b0ab9d86364cd12ec0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70d31447250d409593eed236377ef971",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a28ce4124374f63a0aa87aa31ecd11d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "936b30e18d2c4e728d93b2417154c094",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c2b7753998b405ebe0c3059026f7193",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "479a59cde9504a2f9e264f37b4e2a813",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10b0aefe7f124a5e9aa5d988564b20e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cda3a3a2caa64d52b96ed11477ce61a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "160843b5c1cc465bbe31b622e1bc4abf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "daf08d9aac304284a38af977b66bbf2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db5af56c68cf4c9d9cbfce697113bb88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3048c5021a154c0486f9cc761b31a343",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37a0dd197344487d833235aa1f94ba20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "741d97c4a660457ba669874dbc05c9b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c77e0be54b2412ea4c44f56f5d29565",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3cd1b1728f6f41bb8bfc6dde7b62651d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e946e7706ad46d09e0fc954221ada01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42a29c54bf2b4db38574dd4d5858f41c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11a95d153d444b139a4f4cd723392149",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e84f7ef5d9e45d78aadc6a3610ca756",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cfb1ac9696324b319545097877f47a80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ce846261c9d4acd87e6502d5237ab23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0a28b1301d3449a9c355c0ff2996eb6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51092f76d5414d65bcf1457edb316797",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3833a7db4dc41cb8c495a3e19bf2509",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8394808ff1304bb0b67307c925fe7ad5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cc17720be544c0ea2d5c5956b9ee0a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2bf4322220944b41b292fc891b4d67be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f5dd4fbde074e4483e6d5fad33331b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e99ad6283c3428e9c1c218633896e6c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "256b98f271a3476fa528b60190b65803",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63dd09270b6a48d88190da382bb0ae7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e358d6dd935445a9bbc89862bfed7be2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2fee20781ab442f8aa15410c07bc39d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c4972117e274a0ab288e0f6adff63dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54b822c40e79468b922417d1d1461b3a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2992553064b64a1c82046ea402e922bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51a2ecab8a8547229c7ff3e12bf2ef1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50a61082e38b46c5b7e46416bc8f2b52",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "761222404333412fb12104e83efd04b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8def523e62124073ac9dd5060ee91ba1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfb9b441fb3b4cf4b7fa1579175f27ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d1b1c093a2d4e2f9837805a8de233ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45792e41306341a487e3de9f443c0302",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f37986e17d38417b9a394c1ea483878e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b5388f7514b43a5a6b3097d13f0e82b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "984696dbf9004e81a74a46100d41f70e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ecf2de31f1134ca7bd6f755dcdae2657",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "637fb173a86047a08024eea2112696a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea23bb79ccf44394a74951c6ecaf7e58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9905bacbcb9b4c8f8edd8ffba43db297",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e2e4b300e504bb0a6ff70c90e892073",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6625ac22812e4414a809de9fa231a60d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02ae016793f54631adf27ddb0f549730",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e3a443f04d945938a32a81903bebb17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a72dca169f464c68a14eb8498f2d49a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa2778715dd04c80854d5f859203e48f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5f77c46c7084978b5459e50b1d0b06e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0ab5dfd748040a7bfb6b01ec407019c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "270d70393ae04430a3d0efea4921de8c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bacbac17866b4827b3148c1e0e9470b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a56cb72be1214f2fabb49d664b0e68ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55eaf43cfa99453f8c9e0f618874512d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c2dfcc2a4d24c928afe13cfe5ba7490",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abe5a83ec8d04d1d81d2a516b599be82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6463a625e1d401b96d599f5c898e320",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f34c458c292f4f8d972684c457a58602",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c1b0eab64774ed8a6c3fa4acf355fad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00533730ab40497ca36c99c7e93bb33a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a883d1b17f1f4f809515b94ad86147ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "474d93b9c2b94b16ba2b84caaf78c6e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cb0291494d64131bc5ffc5ebbe4003a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5c0270919f841469d7fc7252d48dae7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "279c6d2590eb4fba95184d00713df25d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73acf89a2dea404ba7eca0753993c692",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a55468b9ab242b1bef85a2083964661",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "362483ad4164478bbf9c3a655398bc1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16d508ceda484d99ba29345a0ab3c6ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69cf4f3037d843cfbd4aff69de121e83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "929ae437926b4454a24ff3e7574ef2d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22823fd5e947490e940a834e579c8841",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e27e8f9b1354b73856a49f26b675241",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f80f3385e91e47e2be3960e86755dbfb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5bdb46ed6541450282ec574aebef43a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c66d022c2c645b892f5171785982275",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7c26baec14c48308d5cd95426df1764",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38efe4149b4c4717804794792fbc767d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2694c47d34ae4413b53f0c3dc1af4de6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59c9c898057d45a7b23c96b42ba50009",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f5063fe7c5048f1b5111c0de5d1a774",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bc17e6695dd474294c54318556103f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32464d86409c4c6abab4988bfcd8bdbf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5b93778fab34f2694226212c824f35a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38f6a85a453f487f92041fc22db2aa9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1968a77147ef4573a812ce8844a39931",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "260ee1edc2dc4b5fbe8d6e85ae6b0c7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a663b45901924f2fa7a27626ac707aca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43f938a3ad3143a9b5fb270aee69175d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "450fca48b829420ab1313260895d82af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b4d331ee80a468d8c14b4ebafc18630",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b452decedc76407aa2f721bb4805b4bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6160186495b14acfadb03ac565cfeb0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa08b2f647d64524a346bb2a3044059a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31d5c591ba27484e8eedc82b278fdfd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "defacad0c8ed40b48776c3f69d84cbb7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35e4de8319074978b987712c5226cb42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a083dbfbc15145e297fda7e202bd432a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1761101285754ada87bc5511fc31a5ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc96c4e81346433eb29e65a50cc30bbf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2217d18382064e7a9f90e53cbc630d8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed0cff53f55643a2bccd4c57b1bfcc67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af9e7945d940431a82409c734ad1c47d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e30878726e4b43f6ac836b11240f650e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54308475bb6547b596c7bb5b577797cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02eb721e4bb249ff90d3941304473835",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7af97d5454bf4487a70447307af0c8f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d50d3eb2b86e47068ca28c884ac82cf7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7057cb2e40d4403bf761041a92be3b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7e6fb0b6c0f44a0ad61c9a199273d50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38110f13643c4e05b40132813bbd615d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db674077325349babea7affeff3eacf4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6440efc4d4d41e69627abe5345ab18e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79cffd025dfb4fe89a62c0a92f49f9df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1314a771638b4bf1a054719240a77ced",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "002736a4741048c49cb9c0fbc32b3c6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7fef31ec131461d9c5888dbf2998aaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edc4f912407f48a88471dd2ad107a559",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7246aa91e3ff47a9a565ba610643c542",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af91128fed0f44529bcca0342e114574",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ae849291eb64875804312fc18559065",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "618e963533a74f14ba931a2bf680aec3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b04b91b3741442efb2a90a74f1f76dfe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf0690802c13476a9e60550d09bc013d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fded4969a754280b342b84f7e12be3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9b9e0bba6524b5b88906049c87a599b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1d81a501d0f4d0a8b9668443b5c5721",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9aa749b531194b48a096d1c17ad25516",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8066d7d2b8a64e768fdf127eb109ae67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c0c71a6977e4d9980d18f270b76f6c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5aefa558f3764caca26814a936e7a272",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94959115c89f49a296162b663b676850",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "836d002ccec04d0fa246f471a1797838",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b514dfca01d474494b191680e54971a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e5059d3eca44cebb43e2268e054fb9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3955932cb3d44f9bbb2e4917691bafe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40edb4008e064759a479cad966147e4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b6019f004ab4c68b288e4c4631069d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5026a03a3ad41198412732b9dab7b35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ef5e9c867c348f58332a05a2be57371",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c985369314e144a9b14a7b1f102e4c91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c1f1560ed9e43ad965c23fb90db71f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3907ff64d134d2e932d3c0592a3b1cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13613ea8dd5a4b218249c7fd075db669",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09bed201cdcd4229945e58eb1c1670db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "593ac1c67d4648b7947c2153944d450d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3f22a8188ca47c2a658a0178d0c578b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c86a615241bb4a74a64a5361d7a902ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47b2af9f074f44488f8865bbfcd8158f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9d713ec85374294a61192408f0908d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46a86a30dfc34cb0928222cffd2dcdf3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63ff4e907b584358baf85780cb0ae6f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e05c0a003fb348d0880fabc0421edcd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5451ba87344a4166a8ebfac02830b92d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3df2ea0bae042dd8c4536758b0b8bb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cad1b7f7fcae4143bf291c70ec62176f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4656b7a0a1441af9a38b4a75a6ab403",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dba5b34e57e141a8a65e123d43e07e51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88741d5f9f3b47acae26affefd51c3fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a71e771e1b72419bae9ecee990cdbe26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79881b8977eb4dd4b3a41d0415442a3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82cb75013c5b4f9980dcc61fbae33052",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82626cc1eb40463f8a34d4683be9b34d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d3ac5c0fb524e71bb64826a84bd0420",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3bb011f85fd4fe18815538cad989d96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06143e44cdd143c4a09ba9d22d582b94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f77be6af29d14992ba10203679c82f9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd2415f6eb914dfdb1a261cc69f764e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8005247f161843ba8e5ebf8e57bfc509",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2bd75b0488164363ab1961639cb55e4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12c068312263456198986eb2a7caf9b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1908fd8c9af844deb770b1bd997d08d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da4d8f45d2e14a1b9ee05c319f90afc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c52b339cc6e541eaaf55c0055ffdd847",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4b88c4b5fbd4ef9b37ac6a5399b741c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07b35770d7d640e89e8383a0a13b8fc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "003a19f381164ddd80afb73ffeb01e92",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03a741d5d3aa4e9d89f1785c33b827ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e338fb2cd0c4af78b5e8c5c74178a90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c97bb9373b5e4cbfa9e15afa6fcc3823",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d59becca71941638a253fae9fbd24c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa7c6b44f2eb453183a0dcd57ce89432",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ae02211f1fc45b78a3b80e4b71a61a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9fca053c32049b0bdafac243804395d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fba241b594ef4365a5f8c02786846689",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dafc2b5cac78475cb6e95a5297d54bd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1cfc9aaf524b4b4da8674a7118a0eb20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7aa2d5f5bab649e4a65c3d9e797a17aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61c71955d059450f9e12d9fb03bbb282",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fb2af49cae24f7db0ddeda3ab56bd9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "194d940fa33e416f91a00cbeb9a7330b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71243b616d194db6a6218213039f0a0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9f39d0db13d452e8ffb890895183ae3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "437cdc143e3e41fabedb810d66a8342c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69d17f3e76ad49fcb0652838d8c0303c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d13a3930435c4f28b7fcf3ccb0a341e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea7d2461e04c4249a49f3d7a02bbe635",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c9be89b6c51447b936a2f9f7ca5780b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb66f54c0d9a432a8454d9e5ed04234d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ae2f381a7d64c129e7e06fc50fe4e01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fbb536c05bf4fbf8eae993b3638cacc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c1447185bcb4361a8aec74e0625fe52",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c0614ba46604aef9f2dbfdbfcafddd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5a115ecfc054bdbb0ff53a5cd89a200",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f3a635c491a4dc78d69a19e5f7d09d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c78744fd870c4aa6bd7d9d287a9c4c33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a4b4eca276e4080a8a7df6d89ab5292",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c405ec681d544509b0cbbeb4b4bc3483",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0e23e624b2e4df49a62e93b4f943777",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2656f646e4464dd6a4a63abe122ce09f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "269938ab4e9449e28f6ce436415b1b8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2bcc258107144e28ac05e55a843863c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10a4a45da0a74f4a87b5bd31b6233a56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd3ea63be7444a55b29410773eef564a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e83d7e5234149fa93c84740b163f61d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbc965c84ae24c749a4e98b36b7b07c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2ca414b431d40949d3cd5efacc2506a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd9fbb800d9d47f780b544bd65e012be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42505b5eb44f4b35b970b3909ae583a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cc9e55c869645d9bac30076114d59ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3001a92fa74249f594f91ff5056fc9d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ead96adfd304b30a90bf6f6c22d2cd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7adc40adbf74367b2f699394cbe4c7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d20827ebcaf4fb98d8cd35cf4ee2981",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24e8fc6543c24cebb847b7de06abf47e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e46661e255f14b45b8bbb90de7d0c432",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e256e085df7148c0b083fefdac4b4315",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9332eb54029745f6bfa50d2056d2c382",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8eb309f28c44f38b7c69f87e0b99960",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4110cf3f075d4ffab4cda7f9c0c61f01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3d94d65cd0742c8a384f5517e53e948",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7632749b8114dd98e07edb2319c1fac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63c994ca86134272a46f1f6c31a21f1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7ed1eabdafc4915af254140ece7e867",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "714d789a021e4662bd69691fadecb9f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0fe5078530e4015a337b9590e843cb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0609881e24b24d5fa80813e608ceb806",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56deef58426a48c9b3cd146f5e9962fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe1cea6e688f4cc3be9cfb56fbe9b599",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e02b5838d2fc4b79a603f305d95c6908",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b999b680c66548c6a0a4955c048caa62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de04262bb34347049807cdcde51bdd44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c08e192f378f465e9b1eff2f908264c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc3f3b820c7a4e88b759add9d9e762ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce50ab4e8f2a4e96a5b12fb8c0dd94be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80bfb6b4c0d7490f8b14f085069aeb00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64d22d10d4394b9c93c334743e2543a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01fecc7161264c0da2fb1ddab6a53eb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1fcbeae02cd44bca0aa790a1a56e67b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b79ee005acb45b286e821b046fca2ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8dd0c5c8757b4a8c84db1350cdabbc39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81970ca350a24429a71444656c5d4987",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a0e729a30244f5797b58f8b532d78ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fa9aedfddbf4d8ca9aad0a555372097",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba5bf2a8427946d098f5e479cd5e0b05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "664455d5d42e40e483f5b484da97d490",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ecb11f4b4c1142ecb5ec7ec2c1371154",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f965079e28194964a9b58bd59f422d84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb2cc3cd9b3647e69b62f282f547a087",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac422fe5e4e14fca83bb363761abc8f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8f0bc3bb88845789254cc31b6a9de73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "92f6c19e93704a13b8547d81c579437f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d230f08074d64c8d9b43ba5e1117ab19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f49c744a7da489da6754d61a64b4cee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2c02bd1d82d43d9b628a76e9d11c6ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "837a87f5ae454f4cb5f32ef5a7457640",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "055de251291544afb6f07e78c5f5d592",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5accb6c6f901459d83e6e1014930a927",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db40fba3f46e441c8a7afd1489e9a3fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42493c0f2863455ab469c07c0a927e37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2bb14c000fdf423188c1dc5e26333692",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26bf4af3c7b54893a3965090492357ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e186441b3a840e1beb514e346bc668b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ced404a572a14ea08ca5de5e60894c7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f648fc3bfb54106b3d8b2257ca2b37e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a229992f29e46b6939d48cda148a327",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "879be42e3d5c42b39d452a7f87acb52e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a69b70ba30854e73894ed44dbfdd9d97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "020e2057d0604cab9f6fc56db4d37795",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3cb24838eed4535970716b5f8bd9fc5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d59aadeb4074b838aefc57432215f5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d82e3503ed4349049d62593cc98a495f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b89ee43e4499411c9d7640af20900b0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bed297187ac4f33b3d1d72777e8aa8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3dacb445881e4f05ae5df5002053ae72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d43419f7292f46edadd64ed4f1177185",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f77ee0553194fdc83cd5c6ae9995e3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5daaf73a6b324a7cab74f2d16ee476e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a961080e40c40a9b5c924686bb9b32c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c49c8a5e10404b95aea51bd42db59feb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cdc2552f238b4547b5fb7240b212683e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "219a0267639b4b84888e0f694c6237f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d03dc82eefe4e51b3630fec0a5411bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87bf0275c59740c3a8e0ca6dbe730279",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e1be8226dcc4956a0c63195662b5752",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a913dda1da14499b80868b23546d71c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71d3a6162adc4270b7fa74e78610e333",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c104b2d15dbd442aa39ffe337f40bd5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88d3f2089f234be1a9b72478ef7fd2a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29ae71b2b4d942b594bcebade282c321",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8227d2f856c24c0583e8d8f3980a482e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd17075b40b34ee2ad7b53407ec4c9cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "607c5e4165da4b76b6ced0de191d557d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6686a2b399bc48c7b9c61f337c7cce08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e109e34f37464de0bb98e9b70267fb82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d001aeec74444540b16cbb393b505401",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0bc000d3ca54adba4106738813f024e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d96e8550e5f44f0b9a106632ff339cb7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4ee6546a7cd4bdbadfa27f71ca4b831",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab0041c18d5241d7bd699136fb05b90b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3434425c6bcb4ee3810422b6c340eb67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3185037c00224985ac3da08244bd4959",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "989d77d255544b72b8ed058ba4f88cc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e913aa55daf14ed99814890271c93d99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "663da3954b8a4d3789a34c036b091d9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "723cb6d3f152465ca3119db359c2658f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1459826111649e3a347de162c57b888",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e7f6a10d6804f23acf890d0df01ae97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d67a1be2065f4be5821c38cd01a8a539",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9776efadb50a42b79235d6f7245bbed0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f87b78aa627f4a6bb2df395705268943",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fa9cfbdf7d04614850479f1decfd509",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0f30c5f0890433ca7a97f81b14787e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8b6f76dd2294c6083a0f18eeb637336",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "287b5c68e4664684badb3837227dd9e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07b757030f4142fb9b0c00423ef3e8c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3dcee9654dc4b65853facc786070dd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "109d4ec697104bdeadd7c65db6b50ed8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20b447a7af1046f08200b8ea7c705925",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa6f9ad0c34c4702864ac3ea61193d3a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "874568baed7943379c7438b66f5c3cc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d58869faa7942b5941dfda12b5abd5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4ec4919ea4d4450b8db379d0d40993f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8925cca3e5b4ee486054fcfa06dda09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6d6b502ab874d0582e28da0a87f4672",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "930932c14cd04fb4b20363a82854b915",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eabea58b9b06473482c8dae75f783e95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3df6576108c445b4acaae07564203041",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6288b483ca0432daaeee66071ed0ab5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1bc107dfd78e4432bc3985fda6a9cf15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fa50bec345942d1a915338b6da0b651",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc501aec340745fdabf36174a5e873b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e67065f716ae40f7a808b980d06205ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a433457ab87e4c8182fabcb9f99eccd0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4751b21805594eacb3e27f827163e752",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c952c28eeea4cf0a4bcc904b56af344",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0812af381cc04f0d9af2fd085463d523",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04d530cfd43f45d8a4a45454f1727dab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1447c55228e1495ba3f34155f7fe5b08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9dd284a4bd4c48daa75e70b0f743a835",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab46a697ef1048f6a37db18ed22a4319",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84560451c1f04f86b98e623483a93c39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4efa8a16810d4ad7b3870a392b5f14a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2b31c7aa04549cca4cadd337e7ab17c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e52a9685cf2d4b97a760d132e9d8de4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e85fd3f8e424c2ebaeeab538a910b82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7027291168064938b43ddc5d8a98f7f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f16f07e26ed247a4aad0f3c936e1d8bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50c3655014484e1285606f0b1034504b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b94dbca3f91d4ce5836a121af5a51e55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b80191b6f4c43948c834f302d2b21eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a074d0c27cff49fd8439d9717c8e2d07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1394b7bfda1d4256ae175c1205fd6878",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebc8553729974ebb9e7033510eaaa0e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4018e5c6f4b342fcade46add9989637b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6976274b58ad4bd89ead5c957a95c1cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbd147f919f543768e0bedf8ca2a891c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56da2aef515642bfb3308b7136d94b65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "888056886d494c26a0f996a79899e2a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "195c728934c64adbb41b3faa89071ffd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fb3b2569fe14fa5854ac997464ff039",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d52fc44f1bc408c9684df23eac282a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b713dc42680d4630a0c01aa30b0c6305",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80902fdc8cbb41daabac9f8b65b85fd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27b0ac14ba884e80826741e4361a987b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7786e20c2fc44b29f635ab0c95f36d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "829f69e5c86644d88f79dc4aee37fed4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9540931dd36d4d2487c95c330cb47ec5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45892e66ae9e45e78dfcc93aa8d28f1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "732eff797f0c45328b48f96726b78c58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14de0e31d7ed482aa5f1733878278df4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a307ee4239d0424db6d44bd28fc4237f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f680cac1c4114e869eda5e8dca3c9dae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3da708429a924a4f89b306cd1f7041b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6c79891333448c2bb6188dea1ccb786",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbd1c810dab94966a844c7cde645852b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3244ed055abd487a8a873c98a7ef6572",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8151e6ad4e594df8b3019689a01d5134",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7db047f0539444da81479daa5338d6d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20d828c51eec4076a489db219f7b4826",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09b1e1c56b7e4a0e810bcb2b30be4988",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bc682ab639b436daab3d1f02d4e2592",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "016876e9d9a54aeb907c47c50de4c316",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b738c82d8ca54767b498813c04a05ea2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "57e4cb08c7c94e4e9311a4ced23b41ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ae8d0a10d1b4580ab14b06b7452412d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9f56b9ddb384091ad8ac487489f368f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e1b8186710d4ef59f8e2ec855199a1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e554f2c3b2fc4428a4dddcf731863719",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d50d9eff914400e9b1562dbd51d87c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1114963600a14820a9ddda52576368c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f497b60fb4a413a8b297ae42d03ad63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9c5194c498c47a78f90bf7daa46ef9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60d2d46a31a943018226b1e7ef0d7d98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ab1e67283e34f41bf8debf7bb35674b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fb67e15c1ee4fc1b3361095a5af546a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8975703523564f0ab216d335badce889",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11e1c7ce8e354647aa1ee2108e9c6c72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fed6d83a61b24dde9477b8c682f99bd1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a960405a43d4540b8851ee4455cabaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "814dceaa451a401ab53280bc7206651d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96475cd9ae54445a9d923f084528e988",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5fef3a6dcbc0430f94a4236f9d19c1db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bdc6c93b39f40529ea49d92e79795bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4486dc87af894035b9c24e974cf7ce33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e3de0a3f0b441ecac049b2671657718",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0beff8de4b545b4b0c831e131f5b76c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b404434085e48f28e5a30f8464df65b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed14bae304894e078587368121a8f617",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ac5acaf8f2f4c90927b27403d9aa0fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1262365ab5c47489712dd4b4c5c4c6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4b7ea3f6a42483cb9152909336fce06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89e53060f6494d5b872adfc64d7a37fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61b66faee60b41c199b823e7872d2470",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ffa9d148bfa44a298e842828d432921",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ce2a87010c64f41a01b117ccc6e2749",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d7de4f08eed4f1880327939693e9f94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4e46fd0a6fe4fc8a3195c9e3f249701",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ed01f9872cd445fbd15c4bb2c7aff8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59ff2c69833b4d7f9201f0c09617ea00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "efbfa05a6a52482cb4c6b29b266862a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa7a0565d2944ed59d8162c6f98cd610",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c298073131142b59cd71bf3207f16f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "962123438c524c3ca5c83ab3614289c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "606e843db62646ddac958d47b1d6a3ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33c1866621a14298ad16c3bb18ada2b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96582d48bf004efe998084447aa795a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b60af30c30524d3ba33772f964e04776",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d03f27e6b21e43bf86d646627828bfd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ecb403e825444d2bfa09409c9b8ec9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d5c6cdd0380473a91c4a0d8b1826567",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36ce245836774aa2949d3ddc4c89e3cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e28b6d68f31497d905e49c0e24b7ad5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b86cc5b14b94da297e2e815bedea4fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff2a4284b5b3462c881a0fbf8eb0b73a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a67632b8769d48efa31c29602c1c0272",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd77a1611af741398d706d1c11341d39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adb2284a1060431483772d201a238096",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0af19a5dfbaa4531819d27aa000ff35f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13f7f744eb174a87b2ab4d72003cbc37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3acf32eb3acb475388814fe9ca78de10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cfced15b5cbc4a85b7100e38d1c87b5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0a120d9a41c4c7ea5c4395a53009799",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "321be59246414d1d8c693c376225df5f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cee99494c31a46efb4d12446b435331a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "529935f00c0d43acaf01c28b5258809a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fda3e303caff4cb690d3b04dbd4cf7fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1a699e0e8944986b813dcded68c9b58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a22ec9080f7e4ef6bda4fe0511d70c7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "928f737b9a4c45c197a0bfc863d2b61f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7931a3f31c24ca1bf8f62286ac93e3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "348e6301edf949c9a815812c207bb673",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "713ee5866e6f4dacb1c15e57335de57d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e8d60f58fe84613a0f73216714bdfb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7e2789485c5419c868947d73d5c1598",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "133cbe7b974a47f4983441d68930dc87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46cb7cb6da854e329af1801f14673f3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce50a513095647cc9c4bd9d52bdbb7c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a8d85c4a14b4ed39538353c51fec0aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "341b11d7d09a4f3d862505541f585a3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ac6f194567a4593b4e8551f2305046f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7627ff51f42c44e09f15291f1f093078",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15813c4b31cd439d8ac26ff2752c2178",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bf24aa3e61643bea8a075dde35955ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb9e14d333ec48efab26b5b3037762f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e52fb30509a4febac45ef93ec03f967",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3dc19cba0db64dc4a30ba216f66a4d7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd1618c400314fd4ae75229d9b023488",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad773dfc207d4ef084699ee4f107989b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f43539c53da4011a592596354254e69",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca0e78624bc84f648ffd35e6895d0301",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1161a18426604625aac24c315b1cf67d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e35018e3afa84e969543300fb5c42a17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "200fbe50ddcf4c3eac8f8ed7e7643a65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2254e0b1b3a74132b2be63e0b34ac7b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9691f06c6fb64428bdb805c9e6a35092",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd05b6d426d1431e9406b410563c6247",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85a7aa6d39eb45809797032d6d284199",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f1d19a8f61c41a5afc2590f9702c3e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "282196d38043492c92069f2cbad4faec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5292417c823a4de0a8b14701e50098e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75182c7a1c924db180c5a631ce62eeb8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83b785410fa54d45aa962f61ff32c412",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "429bcde9259049018e5b1d7fc3bcc62f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6beb43510e84ea2ad9b309cdecf527e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e855e96fe9d045279fde53df97460462",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d86298a31fc4d6086e373c7268bbd7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41c70334771e44eaba727c447b9b1617",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3629afad6fd14007abbb1389746c77ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e317b6ae268e435784ae93afed14b6cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f29a0ba18794d2ea0cec1d16fd1a8e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1edf886dbb8c409aa8833155a2088e17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5735e66986dd483bb223414caea0d405",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bd8c44da3fb4341b02cf2261ef00471",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f9e1a86b16149089bc6da96286bdfe8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a788e53415464d889d8c92775d085acd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24eb140131394d7b8110d43ecd3e916d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c073ccbb40948ee9e18597f46fc468e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68eed566c44548d8b130d7ff910abb64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aeff5cdcb2a64e78b8390affdf2bbe3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39f4e21e2c6f45558065b3b2b1cfee0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66d16d6a370046ef8ccd28a85bbb6f80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "668459d2cd06424aa80dfb58270a434b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d5a3df84f5742eaa9d041d9f1f6bc45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5dbd61fcff44271ba5e8859e6f34730",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4c8586bf7444c03ac2d1dbb0871b0b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28c6f4856bf04275a5c381d1fcd1ae0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c37342042cb45c6bd15e7ade43516f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1687e28dfdc4693a0f1d66fefb267d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ccd104ff5bb1431fa5f82bb5e3699ae0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6aa2d1b9acc4453aaf5e6ceab349b738",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "874f33a3b3e84865b57158a16f5c760b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6aa9ed642cc743a0b96e9136873c2402",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2bace53dfe94417b9827fcf5efe0f03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee643e5bde3b4626a620c677df54da4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8779bc36cf64ed0ad76339b71c52dc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a018fc3528694e4d8e7f6b0225c25783",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eeb8f5a9d5cb44d7b1a4a2309c602885",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe4ab468644d41b6bb95a814b448f8a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "baa379be32ec476a8deb4584c997299a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb4548314a2c4a51b1e7f8eb1319d8d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7485643e32014e55a86f4cf9eb6933c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "714c08faa29f476daa46109e3c2377e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a17e7b30753c4783bbecd2b157c4983d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bef1a7465f0b4198bbb59ccfec7ecc51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "133da54de9424642ad167ac15ca7f1d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49188b50f3a94c6a892e3388e2a1df13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d671a5be64a24ab09a2ecde6ea14eec5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e06043bdf4d460da261569360092913",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "379b00525792431ebc38bc61b770c229",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a83c43c7a9d49debacfe99d8b425f4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "478b5d5b208c4e909f02603ef1a77d48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "172b904605014ca4bacedb68cf79906d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97906ec162d943cbb064b374af320979",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b133bbf339194901a8078c071610e523",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b3546ec3cac46ba98cf6441abe15655",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6de8e657c0a4f02a7ff5b7005e57919",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a0550d7bc954b4f9814eaaf1468dbab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "341335d9da094892aa9c230ed36d968c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdc1bcff03ef4aec90eecc4ab6fd6efb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fe69fa2c17244bca6f9f4e7a45d28ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcfc45a71dfa49979fe83eba4a60d54a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56d01255659b4782b50744b8d1c83cc3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4def054d2d784da49aa537a5c324c9df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d78c850b71444c6bb5c26aae328c4b9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "efb7067a57374796a43ed9e30dd8fbde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2abb0c28031d4cad8333b6f521b2d941",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "894d13ec495a4c48a2685b46ebeab0af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8370b754fedf443292dc480e30cfd9aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7af451e132914bcb9729574524f293de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afbea9838dce4364b1c7d98fdb9a8de3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84a3484517c64f09a468976749560d61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fbc8b88127c14634a49152a8b5e0e2d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e3bb839d4ac47f9bd60cfaa5f13192d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf603fd7ce924a61b2a2e1fe1373260d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de208018d7a4447c93065746db88d772",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77e90c9b7638425c8d10f45f6006cef4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66b005dce3434547ac18c5f2fa6b0505",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52cb390604af4d8eb8735ef52ac8b77c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "118bc0a5a01f45c59313f37a128269fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54f2aeb540ab41a0a1d4e60244eb6de4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14f3625e46254741b20988c75ad1c5db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "695599b3838d48c79bca2a0b66efbb02",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef34be1f0b304072b8368ec6a43ad56c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19ce1141f6c64010aa3ecea696aa60c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "375b855b94754f11af5cab6f3b1fde3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a86913c7c77e47c09a8732aa00be08a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53eb560eea624dbeae4766a863b67d85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "642cee12d30b40678ca0ba249d733fd3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37934faeb9ef4053a64d7c52b3f3c923",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7548d66db678421887df6c5ce471827a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62fe09291a754681b09cb371ef34898b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19003aadb3e14fca9f98d600f0b1c146",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "620e5deb37d446e18c0fb8c1e6c2f5ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0b059f79aed408191bf5b7cc74ccc15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "039a75f82f7a477d98295d25805ff1cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f6060d2c1a04b63a0c8fd72ef9de74f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2095f3df439f4161a532660d621d0394",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9017f747fb544f7e83187ad6d00070f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9c7fbf7944448509ebe7ce24d08e175",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "868656a923114564ac2076941f52c9d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dabbffa42ea14b1cbc40e2467d910a01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25d9437bd6674a97b25105a29fde35c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f804adf0f3334779a004d80780336f46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fd069f283734169a068444685d636da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afa750820f8843b7bf166f7672eb9f59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "467f3bbf022b4a70999b5e7138940674",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98e71da32a924377b709dd92f39cb0b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9a7fab52faa4fe6ab472c0a14f1961e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7db0d20c5a54c208b299edcb4212862",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca46190b991449218099fb587f40540b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d40fb3af74cc4db6be6323221a90b2b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a77447763c4044938f2ee5f7a0f33413",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce7f151f63cf4666845a50045309ed16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bfee9eb35f244ca9f2352240686b70b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d49068781669419db0a043bc4c68f1d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c9cf62e24904fc39809f8738c496003",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37f720bfaa8c4a2ca42186645b83126b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e100b4a5e877424c94ce3cd67a332e22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33841ccc284a48b5a5442a27553a1095",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67e983b448d54ce2a729bcc8f36a71ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34a83f69dc60417dbc37f3eabd8f9e33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "024f13eb217b4b38aca6b09ea671376a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34d70ffa26234b508748912df51ca056",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0ad6406839b496ebaf188a7ce035596",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c43ad0f89114320818d379471765de9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3703305aa3a24b7d95ef1d1666372f19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d95a9b35eda4da3b6ef7a59a4018c76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e833530a370418ca1d7001c554bf434",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34f193fe476f43a09789d597d22b88b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed14f596806a4795bdf3dc0a9c35381c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbedf3459efe4ae1aad109609bf15ac7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1cb87001d0b649f488bb2c0825aa5c2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48413a28f8b341299732c52a4aaa9a75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc146ecf0b92428381f77943f76b51f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e359bb039ed242ebab64da5bfa14c71d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f8c026eb3564af1a42ce8703c302925",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4aea4e0adb4d4f6da156ddcce056ac86",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afff7b556e004924b76a6205b25321f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b0b36afcb3c4c7baea7e5064898c501",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4bb21a08885f4846900c0bb6e32f271d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ed9f71a486f4b69bdf890482aaf786d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdc80cdacd5d4f51922364f9198bb4a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0362f31573e74eea8892fdd22781559f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f4d768088a94d3cb95a3560ce768bfb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4d854ced26f48b0a1e001304ea24618",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4a9973f3da4403fb1990a2464d63b9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81d5b1a05e484abd9df869b7aeb3d403",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a90b8f041157402cb2a310e9f28c6ad1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ae57c95d8ba440d8ea2a28e8e528ae5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e0f85494607477ca34480865aed8818",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a062f55bf6aa4b52a63b18455e8279d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26c67d540b4d43d096e2f98704c2903a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f794f4431f7749dda4bee76bb53229cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38aeff0b259d46b0bc8206fdef430615",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7271583c15b4a2cbfeb9b8b0314faa5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1893a4477fe423b8121eac0c87cef1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "deb9f8ccc16f45dc944e12af90b4d5a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d0ec59797744bcca7c1f119a14e50e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2d400af3e3b497e8d521529ef744de1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9011c9c5e3f64a5f93d12773551d14d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "957abecbf5e44ac6bad4888d09355fb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c48769ecc7c45778f226aab6ff6f534",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a76ae40a3b94fc59bca59610c49bcf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6adc853efb754c1aa5987c94a8bd7380",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cb15b5dd07d46c2b4d3d7be50bd1439",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc70bdb0ea91463d9e991456330e81fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa92ec67c167480bbb6c68d28eddd2bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "972cdad824be496d9d4c115481e75d2f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c7a4ca62e8b4e49a54cde53ec2fc39a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2769ed6180eb4a6eb3f654bd79275ab5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b8987cbcb904569a7846b61b638d427",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8745dd0752444758f3bf6b1350f7cd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32cb546cf67e451d94bddd903e06bd08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38b620bc5e6e49d4826d40f817a17b7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2e3a0ddd86545eda0f4e2ec68d44753",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ca5193a75d74440b6d3c19d606d6af2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3feda08d11074771b42bb27d4075d542",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63ebefc537e3476e9033f960bf9ddb32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e839e25295de4bd98dda6dc0bdfe9c52",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbf7b9f9f19f4ec3839a871e297b2724",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "426c20d1cc0149c3bb693030d5a62e0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d57f64c4536475aac1ea02f18be2686",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df75dc08f5e14c65b2d73e477ad548d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6e633cb86784e16a162d96da5b9b8f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebdd007526824b6c950155f5943f8bd5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e65f65531594ec79c5d7b59f669a75f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17baabb30ec54eec85c4db98e88125c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea1e75809e7a424e9d90f14ccde3556e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7c196e70f734859a5d02f0efc21358a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8921d4253e94090bfa5948433e981e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0fab22fee264ac4ad0d64f81bff4e50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e695525baa244f9c91849a0554ae9fd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c413ec70f6f94414928ac49479288d38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f11af30d073403b9011c4fab07684d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed29859c5274407297ddcd3859973b7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ad895285220401a874ada1c385aebdd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e48776e7bd2144f2832bf5c250d4a768",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbfbcc457b234d82a440956834d4b257",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87c9c6fd42ef46598f46ab8394fb74db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c681fad334ff41a0bf41235da04e99ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f99b6788e3dc4d628f0a6bacc5d9b080",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13c0c558fab24756b09c49d5780ef8c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ffc0664509845babfcb22e9d6382f14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a20fc18bfb448308dc7960eb7b2fb2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ccc461cec7dd4f47a5a338e132391c70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d2e0c7fc3bf413f84deaabbde08e5b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c491acfddb184ec38bea7ea255d42f13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7f9927774c9496c9f738974a23fa822",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3964ad135914b4abab97c1f73de7728",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "830427b42c44486c9d4ffcc689839ca6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2b3e2cbe8f240809f168620f1c7e11d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78034d3264ff404ea46927a869da3b58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80f9592c5f7340e0969748cde44a7a93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c94aebe5139242babfd1f95d02b81e76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "616c40361bcf4572aed8dd8b8cc959e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c80f27e6e2af469facfb285ed267348b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e1b1177f14b433b8cbd16fae3876051",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ec6ecadb09f4928b8c7fe354e798ca8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "924337f8566f41539ccba9e9fdbf17fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "095628db29594e2b8fd8a9277a0049b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9e78aec815547fda2d4ba0cf302cb1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "031760f60a944d6caa88212ac15ad6fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34cc2df568af4f57baf0fab49dce2594",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45fe8495a11e46009420c5657d24d54d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6d7d3eed2654440bff48cd86422ced7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc9d5f7800cf464ca943fef70f6e626a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a767371aa834938ae66c276d14b12b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7012227633c8439f95ded9ce9f896661",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3d06040554b4ac6a2de06595e1e12d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1bdacec23f09433ab2099e824a98817a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3a2cf965a4c4d56adf03452bf3aed99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "724dc94f1dbb4ab3b86a608f5653021b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1a794a7d83b434995aacefb2fa2f7d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7fa9ac43d7c4a61b7b6ef4a9213e4b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa221e6578524fc687ebc039e1c9e465",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b267e7f3e6b54b7c82fa74186d557515",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a01abc19b03479682f23aadb815e3b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40ec7db49bb74eb685ec74cc5a502dab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4dbc254f0350419397b74d265614df4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39cf9e58768c4a0187894ca966675225",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55c64bdb80be435dbaebe76a85a3ef5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "589f35b3bedd4a4396c2b319ed8a0bd1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27832eb954d247779fda1678440d7ce9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad350fdf35524110b012e7ae11a54c7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13b9114bba9148d78fcab6a7fda1bff0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "071ee66073204359b439b4a277551e2f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2679d83585e544f787da49b8ccb0cae5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c638e2f0dab44f29624ec2cbda74b71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2998f9a49db24e4cbf607cf3fd8540dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8aaea4a3d69b49f29dd69ea1586cd898",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28fe9bdd91ec4c418f8c7e1730a907d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb3e639dd1d947489b46f3b6c1171f68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e6ba45de4914a66a66203fa3cb104db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ae20d8715fb4628b630fdc40098f7d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba9244e2e2e545779661b98672a264d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7ce7e70a0e84be5906f176a05dc1b1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38614ff0560a43108dcb18bcc9ac6bf9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5d7bdc477624527bcd1a86f315e5662",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a895a013ab0c437f9c29f58db56b18b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f7074a932834b8aa70eca68f08ff23d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4654444734f24bdcb8921a63e619e390",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea12214a4856480d939b6bddd93587ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97dc90c8845c42fdbb8fe869fb1ab09c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "915f82a2c37943098d7c67f86f439ce4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4868b4e09eb4f72a11f8ae2f2c64280",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f26f4bee2d0b4e98842fb3c41821a6db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e282242b3e2d450cb51d91e1e329e961",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d2ed002c2eb43dc9f2ef113d9da7a34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77bb124cbd914c2192cc68c61921b4a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb7e337500334e1b86aafbd45a55aa98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f20f9afc6094d9db729d98cf91d2a55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80d4cf378e714aa3ba30213065c6877b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e932a0bd788d4658b201d55969af3202",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cf9eca9b3d44deaab1e26158ba0bc8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c42034426851405cbc04b1923e6dae35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52364f82827548069aa769ea478058cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "546bcfd3b6994f2b9b5e4604f7ad88a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76d430d991c94326a9ecc4f5f7713236",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10301e56ca4e42f1aea721b3ee3e108f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f50fc687516641a5b3125c4f8e786d27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00243f6289864f6d9d78aa8412e0d03b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19f8da6c1b0a4b0591eb3001d415afaa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "988eae835fc94e9582fda27b50617091",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ec5e4e59e58461d915241bce9834570",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2357b47bd8824e2dbe3b34a1627c3251",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8e826fcde584ed2a5a1b429bc1564bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df4c8611fa9f4f4ca8428db5b2300479",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea6f37ab5bae41ffaa5182bc8443e744",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a9a8b47c3564764b8f9cf710179fa85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1dbfc206a6d49ee8dbf67c6c3ae93d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a76076ab995e42af8bea972f2f8e99a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2df7b0a3e53840e5a23a485fe5f6d787",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5af9d915839240f4b534c2ffc5d1987a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7aee235193e54cc4b9e2b4e0cf548e27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea28ef17b60a431c83e2d1114189b89f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4386adcc7564c1fb4d9b348d83a919a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ce77905b4984b4db3ab00668fd7d63a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc9a64040b814e0ab1d0450da1388b04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ac07d33b1564646af9b4dc1f5f3f93e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2921e68185de47dfa9700df309b1b2cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38fea46ceb924b419ab61b5af222c7f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5315cdcac844e7495048c0fc3dac402",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c740964c6774719826e37f0cbf6cc58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf6fef483f684989a325ee24166118f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7674cf6437114d539e0dcdb084010dc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d34c2a9010c540bba5cbac6f9dcd2a56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2bacf4e29c94e81bede68d9504ef58b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0aa011200ae34cf48437548f0148d441",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c2faa8badde4d58a057e6c3849c129b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23e894a534094e6bb261df1e84590fec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa46750cb972452a9cb72261415d48b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d88679d8ac7d4368817a78c2e109fb14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f755963b8c874fdea3d4dc3a30859713",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93201a6053914280aeda7cf7c83900fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "08cca29a902e41b6a3114721f2a33dc8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d79cbf6b01dc4aeb9c2b050f2ad6500c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48cbd10f1c3a4b80adc80e8cd2369f3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90280ba2a9b543249ea27df6a2bf8db5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b4a2f19b48649fb924ae9a86c95d337",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b23a3a6bef74ab3ab352887fea0ebe0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b6f80bc72fd469b96f0e9302fce1a61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee677f597cfa4bf1af2726fcb1197a2d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8876d2a7217e4caabcaa375172a2453a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7dcb365d2b944dfad7d62a263fc2ef9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d093110680e4783a9a74b6ea6a4e441",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60a6e8b2e32648d690b8afb31d57788e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7fb37384b47441892a4dfebe54ad7d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d71058f5fa2f4d4c8cfbb934a39fb3e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cd45bb328f44784b3e20b63aec512fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26a327e13cb34a01b1f0aab776fdc81d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3dd6978999ba4c85a1e914d6c2be9167",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d01b3d4ddb5a46ea954cd92b898c25d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8dd9885781a34db0b688f319bb2acd9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d5085ccc6ce417eacb46fe9f926851a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f3bc9ef30f94f3a8fdfd8ed023e6215",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "238ebcdb2d554c149de4981306254970",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69b6908456f44f678109967006601256",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6fee7085fd44dac88cd2b4095136d4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "511f482d938a431fbd2bfb027d52349e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d39765ec06454c719944c2b34a4959d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d0d21804e07488da2fd2d88f9ed4ebc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19f003afcec141a1a04adbde0cc823c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17f03b16e4d64922ac8487f3c6ac0587",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c2167e3c41c4078abd0e0813e84cb58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed21f5d7b47c4806930373ba19828ce4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d18059857de9408f82320628ebb6098c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d52e37863324a578d905443696ccef7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90f22fb84ff940c2b8e9f4fe4b9865d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ff2e04eb697405ba899296a2dfcae7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e75d5954a25445aa65c7823ed664b82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a79cf7f4a02a4f2bafa5c3f0ec6a89c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40d2ea5b978f4a6c942e62030b62a5cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfd43dfafee74e0eab77470fd02ac3d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1a00445d2934e81af81319daf474e60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a6032b5a29141d99de3b925405a31f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c10416be2a9949f6bb5a914abb22fa57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d7c35ebd15a4e348de8d84bb11715ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c31e0ff0ab141529c8eaeb4f094b3a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61f43aa5756e432f9b140e1c6e7bd729",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f248cf8a1b841be89fca67b2a3c76c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d416f54c7c64242a49c0cd0353b4c8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34400d52ff684352919cb3012c86621d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b34708280a8e4d9289ee292e6428e977",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13244793e23a40bc9d91a7b5a3931482",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e839a002db0f4f1987fd1664d4350446",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb359fcd458940b4877030453a54dfeb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3cec784729c54a4fb1fe07015fe89ced",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7aa029cd02b945c892d29e845ff5b566",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6f1c50668e6458cbd73b4607b0857da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "759ead039cc54b3d9af0dac7bcca5233",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5eec1356df9a4a9d8ba5462dcd998f21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68451a1d31244488a31689755139951b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "184b93fbf38f4d5c962f3c4e0b954011",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d4f53ad3d89493ba5e26b564d991109",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25bca41c29534794ac315d9f01295977",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0a8b58f32ff409eaa6a9c32c1ae9434",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7fa29e65905425c94e4bc016489ab4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "418c0d4a9f2746a0bfde3c9e91049a0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fc70f6a3ed64058a658118a29518dc3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74c2b24b51f248f6b1368aa00de742fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e39476c36644f5b9e80045facbd6883",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ee39fc851b84e218b4c469bbf8fde01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "439267b2957d4575bb65ca0bbcfd1af3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c8890b14e38479384d0748c8b6f4a96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84680ed661b64dcab1f247b9d42523b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e074767ebd3425d85804a329f11fb3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56f273334d7742e18423f3b79108d73b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c160603d8ccc41fabae6db6b584be3a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e806d3ea3b34396832e1b73459e5086",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d6777f1194741a8a625bc5ee62c3e7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3278f40a093c41dd8ca06950bff39f47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b581fa900014461a8810c9823606f04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85e7cec3b1d64facb08bfe9c83d49231",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a5d39294bba4721bce2700dedc69c20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f7d52552d114147a056e01767b1c9f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91104fd83a6c401cbf85994191a45afe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e164cf95b9174ce5ac59f990854a0f78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5077bc027d643b3b65608ec71fa8fc5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "babbffe95ce041d293985099c53dba9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f67aad90f584c34b975fee14ebebe7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "126993c2c685434495d714ea4ab3b586",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d498d1be5b04b3ea748b0a386b726ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2cd104ef7574e1aa9d2e7f637ef5082",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e2b4d3011a349c3aaacd1ebf99f4b5f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa61beb195ff4bf091e4a969550741ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "836d71f500bd44b4b676fc1eb8b4ccb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2d17eb8700945a1942f090bcbb38107",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "282892a3555e404e82c4fe5e1841bd93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "596e20ae79304bb5afa9f77a051f0f8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "939334f59f5e49c5b62104fd21bd53f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a88f79dc69aa4edf82873ce9c0c75e0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "566e4056a65042b0ad4cd8e3dc2e3bd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "689601a7fd8c451087425b057e0120c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d2224c6900e4b3f921492dc461c9e63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ded86c8ab3c410291cc0572d0b32a3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0ff45dfd5394701a5db32520cff3516",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1bcabeda771d41598b6d63be32cc54ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a4332a408334adc8820fc87f06a33f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c197a1c5e1fb4b128dac67e405a4ec70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87bf135350a94e0ea189da6d6422bdfa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f38f6af158094a34bb88c167c12ce082",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "338315e10e52449ca413fc2482a95d34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91d783ab7cc84617bf2dd839a9eb6e22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10a2c496c89a483d84718333b50a2898",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a0afe43b4d04e32bb95b396add3e71c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "372f72560ab74c04a5d803974009fcf6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83121bcb0ac0412a8c3536b2a7b4105f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "461ecd9b7e57431fafd1854d639f4e25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "067c94f5992844d7bf40460ed2c2868c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "983ec2e61f6c4d2b80dfdd76cc79f2e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b01566ba2914646a2402978ae3d4a43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b7c6c710ad14ff794fafd8c77e03c6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1fc6121afbe425caeb9b824cc9cf634",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8d7e01050f9441c84838979b538c983",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b8ec8b4c72d424db33454673b2bc9f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6ea1a1011cc4d20adaa3293a0e1fdcb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6db50407f7bf4c48826a7a8fec57b8db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fbbf2977f0a4a0388698337224b1ba7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cfea6a3f76524e9397b43cc2a7f37482",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f4e03f756664a4da3a16147b8f36ba8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "127d1a7ba59e493ca95e5a2f64564180",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81d9349349804870b0f69ff7be71739b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5641fef83719453b8082544719dbe975",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "495e6f2e175043bc954324696db97c2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3758402c765248248635a66e0fa67f0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e65906c3c3d4aafbe288f2f60bac039",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a31b91ddc82c41fcae6c7753017dd39b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5035859a1eaf47ac9c45adfc31b987d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9000b55be05945919ff143e23b04f38b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fa550082ad34cc9a30825d030e1bb0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0be21441b10494f985235fd8666121d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01adbd166cd54a41a0c24bf06e34365e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69eadaf29024452ebd193a13c7dc086b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f264a3a45c1349d9872f054fa78c91f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22683770e1d54ee39a8c9e71e60b518e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca3fcee9b09146b998f4fef73ef9982d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb85906ca63c4a00b1816295e7610c2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fa918dbe24340bc93c51accce85c11c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f577f2ab40a04100a3fffcd3d0b66ead",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a711eb1f82549c5a7d4c540baa4c50d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3804c7c3691344eaacf044bd5c28dc96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a19f1579a9e47c8949f55a6b49780ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0700a060ef944485b79b9aa8a445a0f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62e34fb864324afa87729cc38ccaf963",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b60e79a44597439091b35d65c2f20456",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f50ab11d71d443e2a1b379e4acb98283",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70ec41c38e9e4ce49cdaba7a26f7c31a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96c7723cc8334e43be8f9458d5975f85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a791fb1102c40319eb41b3cfb30c09e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4b6621f64294b87b99c9a70aa6f5fa7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e062095c5bf54937b0531a67f7ada6ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc78c3da90254e91809d21e646ec9a16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b883fd830a554adcbbf425b13c3e6578",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02fa8852465b467caf1faaf91ae34a52",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fcb888672934853a8866a65dec2c0b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "465426f661b74f1ebe144a9c038ef079",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "596a7c80dadc46ea818588858dde33ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d230c0f9c5de4a1f87ed90bd4892bb81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1e3ac9de5364b99804c909b26e25c8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d23000b8cf074eabbe391a272e9031a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9ccccd937de472db95e848d0aff6747",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7836730c627549fbaab9c095ec9915a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1da7d1a258744f1985a8179e3b9d0eef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d39e5ef6eb84be2b1b7633c551da0c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41aa0cf239d44fc99b42b08936a79ca0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec6e5d11d98e450ea4e6f8fbbce491a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8b6e0b16f2f4452b84badb220a31800",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "386f1bd6247a41eda3c44f173cbf0d28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b58f63aa3f5342f8b800a377836343b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87120aaf7a544378b87a10aef727cecb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfe10d0e251f43d1a33eb85a257c75c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c2101ec57414632b4cc148de7e2f853",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e4cea90316547ba975d3bbc84f8677b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1901098cab8f4b2c9a78548e98a0a9e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cdec797349e4f978b91ce658c107117",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e621365f348b4c81907797337a78536b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5609170a28c84e87b31003c6227465e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2ffb8a017084064973b8c667c63587d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b3f599672f44690b467f47a2c71dca8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "119320998edf48a5a527d6797684566e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8684aaf876a847b8aab0fea052169d99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99a480b3a919433d9680c675b60096f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2133624fac1249fa995258e852091a1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7cf1f3ed9b4471fbc3f52f193f1e51f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67892d22991e425bb886b7d8b2f799a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7135dc6b2e814d0790c2e178e9a15fae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b341185c0402455ea4103f916ee5682c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "150f2f3bfc064870856ece442f383537",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7885060a914644e3b8c9fc0985a5d2ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87c50e972e454b1c93328801937ea6e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09d282f0cc8143d288ec6633baaafd01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88741c19a0d044e5b0c43d3f1b7c1882",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a26cf714daf45ce9a9c29b15a96da81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0c611ba42864da3933c24e3a5517e88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a2c7922e43442f69df2416da33046be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58872b213a464fee939a8b9705502e34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ada910762b149488beccd34b5e6d9cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e18041d74369411b9c5f5c7d4d78f905",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f6c54b57fe94576a053d051f111081d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c8965bcf2ae44b0bc1eca28ec9cc519",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f4c509f390a4a8ea07910f6bf91feef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbf8557e473449999487576da2d95396",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "644e84e9b1bd403baece6b96dc0ec2b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c9d63307e554c0fa79197c199f8077a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55e2f265434f4e668a85346c5135a598",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f8ba7b0402b43bca7e35e0c0ad780cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4bf378b64bd0478e86b3023dddbb3563",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35d15e95a309419fa544a350b1e5c50e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "faaa3e5e59df4c66879a513106c45e6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "047563c2d78c4f7eaa55f9016fe95cc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6adb403fb9c649e6987baca575e65877",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3380698e331044abbfbc453d23b8de8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81bd2dcf1ccc4820a302b446b10fd3b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68795e63d42f4e2e96f5aa196bb879e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5170cdf14c304dcb937061e108a30a59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2b7a6f92bee46b19acdbe95e3cf1109",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ad7846c7f564acf93dbd15b42beaf69",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f5e30cf22a34f22b13c0fd3f9eef38c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b49e32d8c2749f995b27318fe924985",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4b12924f2f84ad686c80ebe10abfb5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0af209a8a0d4a60adc479961bd2d18e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca80586da0f9482c98162fa506ebd198",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b5b60185b1b4c9d97782b4c51943c41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5223dce84a2c44368b0ee6d27920fefb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54c1a0569b6541bda39077de6ae740bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a99c9d73d1640558ec2f318420be92c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3b49a76972e4fd9b99cd4df2bb48059",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6cdb1760edc470cae980dfa01514dc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d72fddd8825492a8755dca9032e4d4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b112febd4d814be6ab4aa984470e31bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f37adc6044f466aaebaa98f2dcfcd20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1b3126267e849c7a10ecca21833fa34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "efb37025ad9e48a48f57539c8ca65eb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e98bee88271141ad86958c0aec48bda5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "660476bee0184935b913a811bdafcf3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c2fa7d6312142f9bf0ab34b6ba914f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbd4d08a80974296ac1d1e6bd853b0c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "600e2d94c9c4424daa6b0546b40c806f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e62769b417b64590aba98c16bc23e0f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73c3b515a6734b899dd421b86037e82f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "199aa580cab74755926790e10484c63e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ced375f3299a443a862844cb11f60d0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1bf53c29d0d146369b20b238ed31859c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "520d9aa84a7a4e3e940e608441942e47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebf55f787ee34b20a0633d646eb33a80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e135a2f35a074bc4a76dfa4f85de7334",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93cc233338044d249867af509de9ad6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c84f45ff8f545ad8e34fbf4f604f6dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49f1c040bd964547829dc1a923ee7128",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87c3fd8a5933478ba3336d76629c0af0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d6dd5a70e244052ba9993fdf6f70679",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb75ab65c4b045fb8a5b860731b70f25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb302fa2a10f4806bd9d2f16589c498e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb69e7d0fd1e4813814917ada2997469",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1ff232a92724cf88139a9f259fcd222",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af1558fe6c1a4501936bc007166c57f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48102371900f4820a65df24c7287ee7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab0e314f12ec4f708e70478d2aa693a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af54fe2474cc4ca2b71afd01027147e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8a3720d019b4757a4023335ce2082c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b18bf9194ee49a682e58482d97dcc7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a1e9bdc69964fe3807cbe5b124f5e02",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6495ba8cec94950bddca755f2cdb86e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae7ea58064d541edaf4ab6d89db32c95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae82452a422a4878abdbf658530c3cd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e24ede16749a4852909d459cf7907d93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35bd87b43b3d4cf99c540ba7156ea961",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ffd2aa03ac248d5a872afce57983c6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a90ee3ffa69e4da9b18588eb044ece55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66c24ed0c03248f9a32ba571fded42b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae60c9b51efc493b9eed68d334092dd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "891d9c12cb2248b7b0189191ab0d24d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "749e8d382aa3423c8f4e9d18457c7ab1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "862b4533047941f3904146beb07a06ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d373f9d8657e4c16a455fa4cdf9fe4f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f0d624f7a49483fa7fcda5154c6ae3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41991c1ad9464009bea58d9c91211f78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f7526880dcd473c973872ab2ae9568f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d1a1b8e9cc5493faa46ac67c69ebe4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fec99ae0ce924415bcb8d1c50f9d43d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a396fe08b924f7a95e0edc5a52bf838",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1bf8751eb5f147dd9a4c8b5e06aeb94a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05770ca1df2d4fdaa46ed823cb15ddae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70b7625b7fdd45dfa19f71102d049480",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2587349fa0740afb2f34a0632906f4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "825ee29aaacd455494f03c5b184e9e9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e5f78a4b05e4a598a1ccafafb0e4df9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c12f56d74de546b2a1ed7a3875247862",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a478f8513a24316b2fc45c46fb194e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f4748744aab4d77a8db15d52f41a9b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2a9698b05aa48d3b1f82c163025e732",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7351296998ee41cca8822eaf6c5f78f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03cadaea4c3c4bc59fac0af9c6af24d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0fcc161bb5d4a378935a196d1070f66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c1fda61fe064a548823c268c228a735",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ba236e2345b4729b22b69565d72d519",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2242eb5043349929b6cc6f43c5f4e13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55183e34b3aa45eaa626a171ef88a869",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a115c647d5be4e06831735d83d793125",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "44730d62d1cc46719522c52bd2c3ca7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a9cdc9ca9304aaabd71b90a72ee8d4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03874e14236c438596240ed140db6780",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9029f5b57cb24ec4a3a76b5c27e72cf7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af861338f71c462d9e23ec5b4f5c9be0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20dcb8afbe9a4e7cbcb6280057f674f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e211c0193954c2e816c19c03317e827",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ae6e432fbb74901a6b87d3c07a8ead2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3368b6bf5d764148b87cd42784ff5630",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c354ed2c5d76401f8c1629df071e135e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cd8b01663c24c21b37888e353a1b35a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a6a6dd696d542c8adaca10401a23a4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1f6f3b26f334d9cb2b6996d94ae61a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55017bcb0c00493b9fd56e8246607cc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abee8df3c2a148af9aaacf7b4860506d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2c560a9975b4e67b6812055af3b55a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d02adcf3dbe414693e73965d92bf248",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af4e395d999f43888b69cd78a7ff49d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18a81d46e96247b6a0fbc0f7dcb2f629",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1fa145a45634f50be93d9d1354ae986",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff6fc2600bc4431da4b46f765f009bd3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "056ca4fcdddd4ce68c12c2a6be7eb8e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4822662782c43f293cc24cb1d37decc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88ff5490d91c4ffa8b018a302e762686",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7dfb1783721c40a4b522e44d836ff2ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89606a8c04db4adf959b4e9ae5ca89e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd194176e8a44d46a38f7dd45a10d87d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03de76e6269d4580857a2c190de6c2cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "323a2fdb3b9e4b5bb25ffac3fd2c4257",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5500ce2747a040f08e591cfd266c474e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "699e591e562f4d9ca17f4f9d7f02b068",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "892a7b62559e4302a27be91fbf99ab43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c0599e15f0149aa8a7b8dcda174744c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67e3040ea48b4b8cb6965bd29a131cd1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a5e4e5ed8c0429e82c835f5aea98940",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53d5f57c8b0e473a92ad36734d8a4f45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac29bcb9aa4444ae9fc0545ff445d30c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab60250ed74f4aa186258ee0829d32d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d12c95b6ab034a7b8369af773f95a9eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0030fc7b936463598730ac8ffe180af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f378909f301a4345885e26ea0b0c23ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0715768e7014496986cba40efbe7b54b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24b919a26fc344cb842bc56c496421e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a81302b1af734305bf181bf74d2da809",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f8f72400a214616b46f34f662587994",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5d3e997457947fdb4f6b40636615bd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62b57ad25c4b4453811f274ef37613ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bee30b34ca164d9c897c8548a9318962",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47c297a78373488c8ce90682eb201790",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc0ab0de6e594c4daa5919e644217506",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2df5ed9bb03140d8bfd8642a98fe9eca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac2b7540024b4fb2b25fc8ae5fb73481",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d39388fd8fc943c28c95a307d85c485e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00ac1f2e7117424a9121ddb1c2fa5c37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "011c7571084d455394f647e3f1646d35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "004bff0943f64dd18b311592aa266b54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97b5f858d3c04e7da63a3c9707e3b7fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d4a60f519a14d94b732bbd3d8f3fc8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f55e580381a42148673361aefe7c6e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f359e3bd81a84df2bac2d3ab1025daf5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b7c2fbc32bb474b920bf62471b9002c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "165c5fc670ad452584a2ab99c2429a9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19cc01bfbae6409ea00da7201b73bd45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2830ad1fa42042cdaab25901957cd7f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "678f6fed624d46b980672c9bcf1f6058",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3e95a81bf01492db8afa44b5740a081",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eaf166074b2f4d24bd7019943772abc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be747ca13749464aa70168bd761444e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f413020dade0497c96336047c8794208",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "155e28f8f7ad4530bf193e6064979535",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcb839cef796487f8c5a0943c65ae8e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a343e8c95cfa4ab19ef97892e64e69c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33b80b8a3f974d56ae52152eb4629704",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77a35e88a88a4d24a4d18104b1368c8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a4f57c6ea37435a8a4eab9df2f05639",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42ac02c224154c0a90ef015d16eec770",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0bf959aff784ad5ad941d7745af5a6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51200c1b683f4d4b82a3676cdb02c7bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1adedf2ac4f24858a3fccf38da2402b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c689b0551ec14c32acf55511a4ae6a8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53724a3cfe1340ddb247957e902bd743",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cadb910f41e74bbcb36dccec5a952768",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "451d1dbd4fd5447c90db2b6061332e15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9597ccf6fb584a64b44438771f2c50c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1538b43cad4540e397d46efbc061efa9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99bf4b3193584b1b98001c022b8039e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59c77b8544af43f9a8f1b7497f3f40d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67e0abbe92cb4d7f9f8b102eb9a64c0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6e68a0a5bd24235a4141c5129f51adc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8657269a53c74c04acf164155ec59871",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea522f4e4d6e40ecbfbe9ca72a0f09b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3adff9e69aec414f90dac684499ee2e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33a0fd7b78da46f0afdb3dab547b9c4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3a5bd191fbb4bbe9b976d4b6e3e2c11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c2bb6fd3f994e95a568a4c95ca3fe7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "752a631565934a35ad1448864c3a5049",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "941495ca423f4fdfbdb98aa911c93cd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1750bcee8cdb48748605ed19ed2338ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d972ab90669471eb72411f695f77621",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a64c0baf17a94b148e377646e1d14e5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6245ee4b398640eea86cc38ed8952aab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f6579357993483084f97f3ab32286ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2774bf36483f464ab698d5527748f35b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0dc59e772c7248ec8dd736cf0e0685a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e133b9c04f0412cb06ff468ae2f2a1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62ee264332c14b7caa3bb1b08e226730",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fb7a5bf5e874aaa944ddd5817938222",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3374d1317fa423fabfb5ccabfe47fff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7555be1af8f24f4a9f062e6e3fe2386a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a68ed5ed03004585b5b5a3c44b6e1cd1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25590434ed984814914a13e14a3b0a61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02e51e7918e44d75a3c509b37cd7f8be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ea9ad03ceaf4647b63a0ed92f8fb2db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d64fc4bafa8744faa0067c61c728e119",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99a90487829c4f309a52f5d6af285a24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36ae086f9a1e464594f42dbc3d3b26d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9a112a405504a2aa66463572ad72232",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62904cd3fac44b2f9b9d43ba96972992",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe580ef1feec4237af71ebe479ab7fd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67f7bf1eaa81444798edcd86c84505d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7d40fec571e463ca72154d8d96fe0b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "706510dc6f1c437193b3dd6b8c4cacca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15e78411e5a04c85a67adbe651cd765e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc75952a8778422a8a3bded909dd2d38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a62285939b1465196020b5ba0d2aca2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d51e1164edd547db93480a1fb884da8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf37d3f870d14aa4b793b05b31b1e06f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ddd28f0d8e2549818ee05857b4342965",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3403ea7d084742ffb7e5adb34dc11618",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65ca2c832de7451eb3134a94b20bbb98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd05b2b96a1c49c394edde916fa4e6a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64d3b696a235485f89b6c054d2ea2333",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ce2007ad320430ea14139f7ee87d82e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90a38695a69d4f209b592e924ecbb802",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4415ab3b37794ebfa94bcbe1d3b6637b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0d8ccb5a7fa42189bf105102dbeb423",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e653397d7b9d4ee78534d0046705b2cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfe3605f9f104cf59eb74852c891a7c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "499c1baba0814b92b54e594bb9d18081",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6319700527a4b49acaa0f0be95b15ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afd8008ab48644b087a408e433c9b4c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "282cecb5132544bd94f6c91651001289",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "807ee8e93ab340b9939ab090de05bad1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e4af6138ae04ca1809125ac67f4222a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d2b257c95fa46ea841a17142a2d795a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a1d4ae1143b4480bdd0fbfd0b3c1cad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c29209ed37fb4f5da1513218756eb8c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4549bc0edfb94a6f993d6d657159dbc8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5517655c43f4a70ba5ad8a003e4b570",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93d9e5d61c4849748046e09c59f43de8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "405008b5018942fdae69ad6465c18629",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f0e7da268f04490896735b5a353f464",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebcfd94c787644a09332a33597b773eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6e4a5bc539e41d385ae44cf83e6c61f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "681779f6b9004b79b0f23724795a84ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adea0aafa14a4df8adacf9b75da63f73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5df8650bd114bfda5bd5d17ecbfe712",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e89912d2b5e447d1a3a94300b1a3527f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c6b76d5a6fe40b7a8db3b0d15b6435a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4eac1d9330f040e3a5bdd621dc4bdef5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91d64f38215441788e11a5d337f304ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ea0c45cdfe84e8782d94a828536ff53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38d01b0ba1a34b02b0fd9ec0ceaeefa5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d39c1d9049b341df8f28d08dd1983cab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f80034c45ac64554bdccdbb975d33542",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "288338dc055045b686b6745a0528389c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca6848c449754b8b8c17a96ac775441a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d45cdc454f50412a957d374466bb2445",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8604fc7346f149eb8391614d7f81f275",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b81ef1de9ea045afb83d8a172691d361",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75e7175c0ce54059b1f1eed88a4b6c56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "423c78e13bde44fda14fe8081fd64563",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eda9b6f7da3541b989b837f4f1f538d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d7e8d22c5e94e718eb1228e23d19766",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f784f8beff46484d8b047697ea84dab7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "549a246c104840fb863e5031ed290b9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf7c806bdc4e4afa8036a1ddfe4df11a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ef801c0cead48c9851a5bd8f5424d4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "149b76a82d7e48e8beb04c4fcf98deb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0c9c7886d9c460f96529e333567f59a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a6efeafea334def8cdcf7d729ca336c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c51facd9d0224ea4917d3801632f1ef4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db6f26eb6bfe4e878e77f30a01804794",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19d924171bf44394af655c44259b0f6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31409a10a40b4aa69dacaecf773ce12f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eedb16d01b4f415691a7025c91a5a8cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "414003917f764ff5a77e8d6d2086d461",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9abe6c2a5b3d462b9116c8b60045e923",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "958ab32b9e934359aeaad7274e83df22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4894425469e2453a9a1b7f2db8813e87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf0d45b65f4346349fbf79b62c93eb84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7cc55e17b22142649460be4dd5da23c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c34ca4c52bb34d28b3dacfcf936d14e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a305454e4e74b06a0a4a6a48362e93b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6507e05841e4325885447da2e16a93c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbc83dd4baf04200887c4ab64e34f225",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abd12be5c1e341c9a94662bea6f1691a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31f3c5be0f564d6595f663aedeb4aa84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c25a3d295a8243cf97a1dc53cf6a17b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9811cb8c3ba44a08f38803d77646d46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59aa532c36314e34bf3a804d146a051a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04f00dd4faff4776b4c999c89b190531",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34a80d89e0854b1a966dc8d955c770c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2d82b4a81cc44c0a8cbb02e919c6091",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fae4d4a337d4acdb4b85c844b5371cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5456789da15f466bb0439b8413b0044b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acdd340e63c14a629484decd9d1a8978",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae82908c5634496aa7b88a4c800fab74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2a56bb240a84465a73188fea8cc29d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff769263cd3741da9b4c013d435ff1c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1904a4db2d8846dd852d126558255c56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d59a30e5e6d44f8ab54cc5074162d2aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ec2b91815314566a378dcabe0c11c91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4eb038cd48d474784c9f942adde5c91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f059eaa852e540fdb914418d14e8be6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9d7ff28a89943e2b45c5f5626a70abe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53d6139a7a044cdfad75bb4a6144d480",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cabf884c8f28406faf8084c7507c3855",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ac7cd1e012a4256ad86c7fd0c50d067",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9430c4a22f14f2f968fc076803fee53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "374444d649f34b5d80e28c12401efd77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc6f3c875c464434963f77304bbab268",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2698f9a662b74084a01ae4bb8c78a17f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc3ed98708204648a50ce048e3dc8c5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05b86462930d4dd085fd210996bead6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5c98ef9395647668ece777bbec06ede",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b323623c64b4864b606f4cb083e3543",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69c5b8ff336747429e5c7224554fc2cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87e9affb3d6b49f9a8c57e966371295a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ef9ca1126e041a8b66fe536a448fdc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20f37267a3144a06883398b2f5952c3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce87f928da584159885483d46fe45978",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8be276d09ffb4951b8af0d1cf0ada0e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d22540d214d14b04a4278314771b1f19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d328f24b064470e93522fd8910ccccb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17e052953da543fdb79dbea9e8fca2a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca93a9ad386642508a34249c103c196f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "935c5a9608864ceea8bc99aaa409fa81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b926d3a0b2c4c279b65913a88eee395",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "771e6e98450745f5870cc3d0ea512838",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6286353730464cbbb920786f5c34ce8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f1e9e87e2c046d29996106d4ae92fad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee05eaf2d76c4b8eb7d9681b042f6c94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1521e66e46f74740adfc1e3302099dcb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e6ec975451f4866a45d88b3ca66b6f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab0dfc9ec65d43cd86ca08375b6f0d20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b4b672f9b784b3f9f5a4cfaefbf1fd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ec0d7b2565c4f57870c379ba5534939",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50d1c08f97094057a31ca368c49f33b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a6f37596af1453d82df2f988b7226d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2103949287bc412899fb8a3a39c32c83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0a6a6f3bb65466fa24e4a94240c35d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b270c07d2ee14155922189d580f3d018",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fcd945d2acb4a46a6d3c8d1e3cd77f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef9f4c93f0e943a39c70f5b96ed6af14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ef0181f27a84f90b04ebda3e8dc6ebe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58e4a73c65ce43d4aa08ef67dc4a1491",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94c238a1d198434ca7a5874bc1270bc5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "112ba82be18c43bbafc02fd0601ce63e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac7e3d24a4834271a5588dcbb603df2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5426149c8912490383b180addde75a2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e94c8fb80a6d4c07b70cf9248dcd21b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3f99e3d3683415085679aaeefc1b09a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd823286dcfb48b995dab03409549434",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28784543453a4a09837f243d98b5d845",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e13ac9b32cf469f84f57628a1fca16c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf6eaf85668a498eb697004040cfec14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8fbbe789c58488d80856d8cef2228df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb872c1ab4404fd99276f79dca83ac39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "762d806b6e454133b2ea79a8bc111acf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74cd467bd57b4813be13aaaa8ed18c52",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "922cbe9e8c3249308577e8365b91d576",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de2357d6bba642deaadf051a2f40f956",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebfda849e43e4cde945acf44642736c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1614a647ed524c24aa3420c3cd31ca8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1300f2aeb0ca45219f444bff207409a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1176b9fb4dc3430ebce275afc33525bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4d5f3132a6a483cab158548db026292",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d140c98963c142c183c93d191b7dca47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cb9c1fda2e74b5cb5c3b56c865ef6b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adea726d60d84c2eb3a35e3b2814e02d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ba8475f8b8e4cc182adc299b1685d70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3f482ea04b044959bbdc99fc9eaf781",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee95f178678749adbeea0432a6e91a67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9441551ff4b1430292b862b1c720e969",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6fc1ec75e7d499a944721493e1a183b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f43f3064c18c4f7aaa40b9192df2bd41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18ba558eefd54965bb15512414a0413c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8082ff3b1339467b95a9ad92b62788ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9be8584f8fe64c5e9a5b43101160c46c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8e74ada4c654c9bba981a0e249126da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56ca4ff0fb0c4a40afc7123d9cbc1d45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1f305d6f39e4efdb8fa3bd5a149153a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9cf9055a52014ee59a64850a633af8e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e050020c51b479881d777b56e05526b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4866b0471714f9ea6e218ac557783f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1ca3253fd44497496b2946d554011c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5f3cb9e24f148e7b2ec436262fa2a4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ec8549076af4ce7b1a77b3a449416ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a6f6aa910e04650af98ba6c5956ec26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38a4b92c3caa451f8a4034fc89d10bc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8410a6ef95834012826e4ec99b9e1731",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ec305748b864c85b9994f6726b574d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce15f9e971e249e18cc3bddecf0e379a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0832ae057e54d8faded59214457bf43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdfa434fa79246d288c031a8576f9c0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "288cb72e666d4bbf833243bab49290bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e5570ddb433473dad0b0a085592c3f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cba586e80424cb7a596c29d0c0c18a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8dc982a2faed4bafb260d7460d332a47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83c4e2a487aa42389845d21e148c9159",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90b76dc49f5445279cd5b07c9636da95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22d1a0b831274f0c9dc0527cca03e737",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "979954723a3245a09eb1d4b8bf5e146c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7417b6d3b57241378dff1c43998cfa62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cb288131cf24c19bca1821aab635870",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4074b9e86cf64878809fb2c90ecfa807",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68267b45041441b8b262385346805756",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e81b0dd23714456da2b945318c7de98e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40af1eaa4f1446daa1d1d6a131dfe2dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c6d241909b841f8b9d540aaa6f36589",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9488261d47e0456c8cc4ac07434c6930",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45b0964a47b04d7cbab6d19e92115b51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19bfce877fd447659f76efcda0abd2e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a32a4b7cce174d6d92ae033920fcf46e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cb672152fd946f682a1d0f89c058667",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e9106fc27134e588d9fb6b2c4a35d5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f612a40e1f5c4bb2badd0c8c2f31d554",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7c96c16aa1d418fb38f3363327b6705",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f63f2e298ddd4194a74750cf43a69692",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cef2d9e3d28042d182b81945c1f8c721",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34cabc59454c4fe5a4fda85dadf5ffa7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2d15307dbbe4038b54fcc828dde60c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fe1fd2038af433bacbf7dccf54e7df4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40b3500fc4354a358f510c4177c8c178",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f8cefd1b2e6425ea931756d11b78800",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d890d34e7f764a8daad2d730afac1920",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fbde798fdf9040b38882366353577b6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ece414f529a49cea966a770775082f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ec5aef902e743699332ee31383711d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4115ea2e856a424d8f8b365019be1d10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1de96800a40a4dcd8ace786d4c5c5400",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fc52d588902484b8b69ffc9f47d36bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d39647a5240e449c912779826a3eccea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c57370daba9443485db4b7035e55d98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6400c7c862994fb18cc592053eeefd34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5b96b115a864f43b2e17c0db4808f2f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f58efc1d07a74d26aabc597b7cd18e39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fe6aaf0f49049d8a421fcb3ba0a0b05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "350e8676f86549f99c5b6c0046778188",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8956e3f55dd743d3825eb52fb3b22bd1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47f6aab2dc2d4d25b85c959ff07cec38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a026d3f3eda44b59bfe6f44582aa0ffa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd0366fad2e943aea0246db4266892a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d5186b313a5405887387cb542c74f78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6acc127edd049eaa15c487f8e6ead13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa95e8ae169e496d86a37b8bf811cae2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d44c07b2b2f4895b92cbb696fdbcc6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15444efa25b14ec78a36f1b4712fe69f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "92086be2cd734bf3813451476000b855",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5466dc6882b34a80bd4ef3dfabd8ef16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e5c497979b141489726cc4b4756ec90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4b2c8a2c0304865a57e4e53976e1dd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8dfbce121134f16aea5fddfa5a930e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adc05a96381949cd8031b32949be183e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbc30e009f024fa4ba9c580e00f4c063",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51c2e0ec31354653a1c46622061f1f5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "426eadbe3b564e72b9f2af497742aa15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3768db7af73d4a6dae6e6c9f0016dfef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c17dbfae3424ef5aa5a83c9943ebaf7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cabe41ac41454d2eb7c045b47e389eb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0243429c33ad44cfb49373a15c0d1d53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2de130ad52f44642a73e717afe42119c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f795c22f97464e8a98be578dbe82f7ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0f88c0938c944cfbbb78cb36971f186",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91ade50dd5924da4bba829eea492e65f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33ece08a27df4ba7be5eb75d7a4910a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6734329315e54620899becff25a14b28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "317cbfde3f0d4c7997f88fb37b9452f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff896ea6afc9431e80ac24d91f10a4e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97cbe9654d744c5dba69279c98bbf654",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87fbbc051c3d4272b5364e042ea59506",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15e26548886a465c96904d8308ffa3e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16a66add46b34cc2a385e6bc19a5421c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0d1993d80134cf0a23781f0d54954c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b684da4bfd3347fba9597aeeb2cbbb27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2729c7bea8543d59b18b222e22c8f6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99538a52b80c481db976ef91eb99f662",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "976893bc88374040abed119ff02cbd00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf12ad5bb7fd49b9a1245c2fdbc14a77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb8c4e7666c8435c88f6a3cd38f05fc9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13816457a6984de9ace2dd5745283fae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51aa02873f3340979aa0276c34ed1e90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ad45f090db042b5bb73ef7f7871138a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2af953112a3342099e7b095eaf086cbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e9282c02b2d4bcc8559c19697a9369f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fee000ae79f041579666a8a6bedd4e0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8f6230d12d84c72b632f4eb835b02f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "686fa6266ec440be8112daf16adadc5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d58ec1453b5e46838ff8df972d4540fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8e2d4716bb047789b0d7b7372fb7b20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe588a42701448cf915466ec0a5dc914",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5156a882d93d46f5ac565aff5fdebfb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "020fbdf108114df5b17dee88c4387d0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6de6528d8efe47758a081a0dd5f55f8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fac8d714006462e8f7c2ac55536bf90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59679e6c3f974072802a73d6ef9c4310",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94a20f557be14b62845dba546ecaf060",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63b776a8b06c484abbd7a5a1faf9d777",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4a4ce36b01e4b30aea34ebe37d269b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a781f6b45b6d4390867503d19c9c89aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4952bf12180f4c28915d69fbe37e3217",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "801fb35dc57a401289ab36b11c8b0d7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e363ce37b3b64cb59054d3c717b532f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3be93a0efc0c45dfadcfc78890f021f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa4a1b55acd64ae287007d37b2d43320",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d73a4f0a7b8433ab208990d7832b82b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bd9f5ee2af8479cb4c3ed947fc67b43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76f14304d48447af923712f73430d85f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb596595bce9470280841f178434a449",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9743148f6ca54294b452f4ccc626dcd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38fe93acb85542a690ee6a434947e95e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63459e57e2f84352a85fbad42c296c30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b41c7277a49420e8c835f73283de028",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb4a9ca4c2994dee93d7c54ddf0f128a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b44d6975a444fb9ab990b7fcc888413",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "736a64bb04e740e2999f1dec18542d56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9607aa58fab4413a1060d95a63349b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38cb9352e474430b9bc0a3d3e5a95b2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "129db185db9c4a9e9a8ee28c262f897f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "597856af4bf345dea677634677f8067f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2175dbe03d4b474fb95fe5b09fe007b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98eb4785049b4b96b2c240f4b2988b66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e133807959734b5dbc7500408deb94f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41424aed3c4e4bf88c5e223a582782f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14c4b36ec41f4ae294a0b4c26fac593e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77bafaab8ea643c18dc59c313c46f47e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c912dea81c4f45efb9cfe3414f963d0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "daff110ba4d64d2e90c06d60cd21a768",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62769c74d62245a994b5bca034e88980",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98bcc30f26e34c5ea07835a670df9c2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9701b611e7a042aca9838177f020b18a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ee98775e4a9471a8b371b24878e5474",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1ed8c407da74a948949fec1ba537b74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47deb53abad14817832b1cc21520e65e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ba5917648d14934b21d1e03f9e1a96d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9edda9aed75141b6b3055127f3c727ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fd7e3e185da4a6bbc8c884028036e99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc283a7df84b44cfa8eaeec989288978",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1762fd374a14235ac561f2724a96ab4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "535d28d6e2b841d3a95ac96da75ce948",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af8b2b08c5044130b6551d9e8a814e5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56f81d6dd529489aa51a832e5a4c091f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbb0c1eebf8b4c599d7fe83e6b3eea84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5163c5ebd0de4f148e5b89510bdda1aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db513b4dd509474ca48630b70e10eb05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1855d10786dc4ebcac5f362197765ad2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7274411ca17c4402a347a45846a9f4b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9555f878ea5d4c5aabe773a35333092f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a9ac94c776f414eb7dc94dfc17c58e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05a76703172c4bb7ac550278d9eaae05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b62064c3716543749ef020b50749e8f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63123a290e204bd392d8f36a64744571",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4de57dc1f6c44dc4884e156364affb33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7369f8c475f4c1289fadbf3ada75519",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "658cfd4d741c40bb8ab5e435e6690dd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5460b17a9d484bb3b785a193d9d75927",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e039073aaef24a1a98c3999231a329c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81af13da5b594802a3dec90ba50debbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d933df259a64c2ebb9a608076e752e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "540afc66bdf64ae5a604d832010542cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d21c51bfac794c3490cd1afdc5b6d5a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8a72ae240ba4e1ab1f4d56df17203c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ad743e7cc9a49aeb0fc7ae09ec758ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4614d88b14574cdc98fe136cb52c7601",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "315758599b60489eb607f171e301ad6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e80ca825a33b4b9abb49b5ac097f7b55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9037c81cf684575ade2b7c95fcb7e2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8cc0439ba724247b3161f7664e10fbe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "483d970920b6442faae5f9003088e9c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70de3611d8de4d608a14282c465dcb83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afa742cd68574d188961a54036b766e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cccf5dea5f9e40739cfa99b8040edf12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1ab15b0d8c24c08bebbdba408acd041",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1db3bda11d10412c9c80c388d37851ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5ae8af1e3cb4987aaf7c1b055e696fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb99292226cc4de0aa942271d608dca2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa4c4cfc04ae4bde8f0b231d946c7b83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abb03ba707b04f3f898bfcb1bcb91d7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8f728bf1d374531aef2f1d2499d08ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b372612a0243432a9cdc3ca0c4de56a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "538363563e6d435f8d7ede296a5cb870",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60ff3cffcfd947f0ad6feba835b2d249",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b34faaa21504bb1a8e71d81e1cc476b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70b55a9024374404b39182c3f033ae33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2726c9bff0dc4d20ab075b521c0931a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d9c14e2fe7c4238b7444fe0208a694b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b077228f401d416f9606c62ec028434a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a3cf161086c44c5851bcd1a2af3fa46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e8b1eee27c6438ca425615d6c2f58c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c440b1325ce4263aadee995d9d37a61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a4dcd3a52864efc8c040bf41a629007",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbe859e40d154ce0a0ff876631740d75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0203a98ef8d47ba8266d21b6916132b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa100d34ae124c58884d3f338ab972eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59d8f7d821e34d50b01323485a55c703",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58c0871d01984e3086f0c07d087973ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "142c99427adb4effa20b907f60038b50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4c86249408c4feb8cfa0f85edf0ef12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3a3f35bd7c0418abbf610fb4b657bd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f121dd2345d5455f86d67da4ddf5fae9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71e39720f3724f03840b660e0b041a01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98005f7f0c914c8288833763320e0ece",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "068815afecbf483da1e120b0b652eed5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c4c062e67a546a8bf503672fcc6dfa9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99be9778e43647aba188b99a8acffbf3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5545c2f4f84440498184113a2618a58a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b38b01e42c1147de86fb9c6ca5d326e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "909f100e46e24f0e829acef4e15757e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a8c2cb0e8e74671aec18d89fd9ea5fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbde896f200a433789c53684123068b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3aa55025c7e14360854999b5d4c5150f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06723d0e9b0f4f15b4731991380e8508",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7d74ca875444443a6a6b587553d8162",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00c33a5a7a0f42c68a88bcca5397143a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da5adcc0b5c54833b18dd2218ea97964",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "602ba813f5f94139af793ca1dc5913d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7552de69b5064882a2450342138c73dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "901bd4a61f6a463fad1a1b53ca029478",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9cc79c32a4324d6e97aae61cffa0df1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9aa0169cfe1c4061afa5cd113a848496",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ab8e3a8746640c8b6c9e3733ec78461",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ef005a16ee84b92b7ef04008b21321f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8e316f3d4af43b48c7b53f4f6b5a536",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd3156bf048d41a082d94e7c0cb3d994",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9dc9585776d400c802da0605db487c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5488bd4460f46eab61a3c7cb8a6cd9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e16d373bf1604ec8b3835fa9b1db3ea9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2955081c2e2449f79786002ddf158bb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8186679ff798455a977f7e2f739ef2c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ede60b8abde4af1afff3a1c0063f4c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28562df812d9404eabe0f94023a5c195",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "395eb06da8e04a98ab0d25df89173b7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b70791d3e9044c9a147f8f0c3baf2c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a0095aca6804029ae46c3fc9caadf76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64d5ba5aa6c047f89a9193f988691bdb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9408f76e39343d7812981b3a4af4907",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53c6adc9b3aa41aea6ca9bcc0c87ec2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a14dce4dbbe436ea1e653207bde967b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc7c542c692b4154b5b8ba98631d56b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7659b22fbdae4ef99ee32e241c90260f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32d263fda6f24bf6b75d911b99cd61d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffc1dd500444416a8da87c7da03b1f48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91e4d117a0f44a07905939f2a020a71c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "739beb4174ae40b689f851dd77538128",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a789baa93b745629856197891c378c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b17490db3e24ac38e99e2836f3c653a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7d9e546213c41eb99b908e024637c2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b8117b27b0c412d82b06e3e096385b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22549f8d76d94c28bedc548221cb3751",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "255a73acf1ce493092c1fab630839f59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6679ac424a20496d9e17d976f55523e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "464dd9469d2b42d1bafa1f75b6981343",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d351c2635e1e49d3a1c7ff11a1f08723",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64e599e9bf1446d4828fc454b082dcc2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "341e73e1cbf94129af6e15b7a5ce5b82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d23919e921084e06979975f22f992f7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fba50aa515674a0588486c27700c0064",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0dac9f02ee5e4856a843352df1963cee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3cefe203cf54708b75c135791e9eee2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "389badbc59d64acca50fd94aea7f24e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "757006d6a71d49cf9dc228a4a7addbe1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1367ccba684a4a948bfe6e40a6e34cd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "92182c6b800440d4b31e2cc15db94320",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17921509cceb400b90d0f59a624b2550",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d95db26faa5b417482b11c23654387a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "57fec3046e234d308a940350bb00b89d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c8579d3c0c94a6e89e9d54c322f0012",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e7339b7fffd4b428ed3484c9c7c3c67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ea51ec305354445b33818484fdeb514",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "decffc8704194cdc8c07e5513e2498d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1d7437e1f064c93a945fe2777a4eaea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e48ec7ffef4249bbaf36a2d25e01951c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83a2f61e1b85496783c943d35d7fefa6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edbea32a763f45d386cddc44c76bf678",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1fecbca916e4aef906ff53fc3bdf5a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6ec2240745a4049949303e3e310d1db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fda58b1227a94ef8b8266001fa0e1b86",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c2e1ab0d78144c59e38d5ee22c7e55f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5d1a4d7c35d443b8022ccc0d2abc953",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ca0ad04b78e41bc8921b1e1b812af67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eeaf6f4d4aa4466fa32273be160a9244",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51dcf890fe8f49dbb23d5b67ff742bba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e63179888f294167940e3d8d358c158d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "438032f932fa448589891914cd8fcd77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "066464f8254341ff80c163909f21f155",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d13bef2cb754ee697ba52a7ec465db1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6c2e500bb2e44fcb2c8531e9e7c2756",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17c668b7321246e9859f7b1277917723",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "087050c0801a477ea3ed1b619d521250",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06300b0d56154a0c959c956bfc5a7771",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cfba63d6e9e340009e395ea6c7ef435f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38c6c4eaf1184988916fe05b6a099f33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a35ee8ad63a4ffe8600ccb630c5544b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc92464f9d2d4ba9a3e457fd86c35366",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "713ad644a20a42ab98524b581a2c6a70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97c55e1bcf8d49418fd6b2d48d192597",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "239d3383b7a64fab9a354ace072ddaa1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fdd08e568fa44c3a2cdb9951d17334f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24aaf72ee5a3465e977fb75296090fe4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e046671e7a564e85852dc28622e87f41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "092de5b90bd44296aa63e13f0fa29b34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba6fde06a77147bfa51b044bf8162126",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e8bad7a220c40e1a09fc50aee3a4c43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cc6fd8762934b7095c6c0547dec9bb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d175124c89a04405ab2c080a405c4e2d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "845dd15f39404bcf9aa1770c4f0c9b0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcdab18001d44f068c7b8f9143df50ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d17bc5e6e88402694975ddc8ad60a88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b42f272174834606b585cd925e0090f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3a433e667184932ab8e56709b12721e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "841c73bd4c04491eb19efe5731849dfd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0eee667470924ed9b5222fa5721fda7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "064584233d7845069ac1eb40996fb569",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "419df93f851746e9822758b1fff94862",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3440e359d3a424799502c5b2b81f2e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6df53ee95de44a187b31655bdf03378",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cff5110e7dd348cab319665a4dca06a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b922eabb002744e794093982fe6206d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb3acc572ef144c2874e47c01a2d0709",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bae06dfc61cb4af0844a16f5e5aff93f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e669503e772a4a8681e5a68e091dd92f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f817b1efc15248fd91d3c0f1b08f5ae0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b4448fa5c74445d99274aa861c76876",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a29d66d564f41208310d34fee10b29a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdbbfa6b36ac4f0eb22cfe6cd1a249b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e0f9fcca24946f18e692ec3f81480db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ecb7327805fa4935a0b19c86539c0228",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48d1cfb72fbb431fbecdc655d501bc1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94af6da41d1d485a830e47cdcbc6dc9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f400e94a02ef4ddaae414ef124386f8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31f347855f944a5a9ba446e8adf28143",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21d3c2e653ae47b8b2724df808defb93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bb44be236e6471891f4f1739321cd2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c508f4cd15bc48ea8b2ddaf68cf18cf5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "429573deba2942df9a774bc82436f938",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "285b3d99bcc54f069524816284484494",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c63f94822d3a4105a4705c3556b3027c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e239067d47047298f93dd649b2d9c2f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27e5c3a83f994c0186039378bdf90ffd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b51c4ebbd8484b30ba1a47d1f4cade95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f49564386c77421c8300d370087dfb90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa017ada40ba45e38bbf793dc3fdd665",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90e4d759dc1a4189b9e7816404af800e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fa538f764c9461585f05c9e4c2efa95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9123888d52574304a0e2855ff143811c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a0f0fd3d392458e99e4f5a00c4f1d44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20bd28214ff4423ba03b732664f97e84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebeb929fe8e84fe7a27de748108d10bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18ae5de7523f4745810ffd29930d487a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "605c9c5b74cb47409d63298f4accef48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2007763512e14bf8ae50b97f2c2fa69f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "860c3d152fa74ffda5640e5ee73eda94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0387a12ba6d24218a0d95932c16b9472",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff7494049ef14d37b64d7ed2fc042d75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0afdfdcc1c73406c9f30e86e7a29f387",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97f86460511e48619ba1842cc5897122",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1551ab9a58e54d1aa58dcd8548fb3af3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0eb909be32054c799b6b2b1327a581e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83cbaf180d54406ca607c16abd1220a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e81dbc195784db99d8671259a5c4b23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81594d87e0f749d0ba17a0c27df06d56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34f316ad478e40d48cc88f038494049c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7e3d2478fec4387a4e916165b37fcef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9d4893f353842e8973f86eca0d9d54e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5962b038e9a04840a7b022f2d06c6ce1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bab972abed024eb8add65fc54cd4f7be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "012864c4b2874101be551edbc683717c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb5dfba0c8f64ef0b8b1d518e28584a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc05f6ce0d494c0a8f7af27cd0117e81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdcf5a5889e343e59b208958c947427a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1cb6bd7c04cf41f389986bd38e3d714a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cf18b1854eb4e7d87d4a415e6891aab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f8f8ca54925434db192055fc5de199e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "472a6eb80170426c8f7f24a86a97d8ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d87a7de612940c4be708f34243caa17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef67ca864ef543c994e94af096f00559",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47d441ea7b384ee9b00a374875dbe5c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ae7a10fd05a4b8dbd7954f1ab68db95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e24e7e918b84b2280ea05b6c96cc4d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b07c803aaa84578a5d2ae5fb8e5b839",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb146f40790d45aab462b9bc3fa83311",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "667db9a3e1e548d3abc71cb592743137",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "152d9e841b924246a96e817b3960a534",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3631d48d3256457dada8f3f40a176630",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95129aed054c4057bea18dc328264b17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df68a04a781449ccb9bbb9d7b91f3619",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "171d9fe2277b4ddf824fd07231b87f7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "489a56154f984f22bc1579f4ef6f5089",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f752ede200ff4e548a4ea6b515495341",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02b32d5a1867418ba9a5196be6db2478",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17c61885781b4b4dbc9342c7827e2733",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98be723adbdf40a19e68e7f4707f4907",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bddb10781074de39d83dcf45d70ced9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "916eb278750d491ea084ddeb554911fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac116869a947436abdc3adac85d76287",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fb9cd29da174065a353af7a6b9a2531",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a3bc29520a2420884478463274e92ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf3a188733b9466ba8e730426dd14fe2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "796f5ffa66944305a070413bef9802d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6215926d1c6c4584be2f75d1fa61b324",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e898b95d098d47dd9afe3edf0c005d04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5815f478310426f9a92cbea4ef044b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f46f3b7a82a44c795babbe7ec1e923a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4bf6195f85240b28fe9fc004d81e1b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c807050c207f4218ae9836cb5f9e14cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b0ca0697a604fee9bd7bfc0641316de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6447df0c96434835bd45ae6384635ab0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20b0eae9da414916ada5e90a7bd80e60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0186d3865569456fa3367ef058fddf77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df28c1deef8d41e99d7adb2389af4b05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f294d2004a942ad97ff1aca62d9e576",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d953348a26484c80b81892c7f22e98f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47f638e83b474b27ac39ad1373e4f765",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a0635e2ba88475fb28f6245b4ab4dd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d8a10f082be4de5b91d193061d9797d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d651284db394237b790bd8157268527",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34a419340992458784d2e56b4b31bdb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f085b71308149069db7911af3049371",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3e43c22a40e429caa2b783bec280080",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc01fb646fe44f1fb7fed3dc02c70b03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3713f92f823d4578b2ca420c268c0f80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4e0808c590f43acbfbb3c82da9890f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "231739872dff4c6dad3eee0796f46d19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1113a5bc80984912bef3dbe11b9db1d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cce19b2a380499a8cda7bdb117b9c62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a90e5fdc113469eae332469084cf1be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4064a8b8b4b4efb9f297bafabd6fa95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c70d120124d746e3a9d778573248353e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "390f18a29cc445a2a525d3371e56a288",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd2d5312ea5247bdad925c438db4731b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38c82ff349e44e42b9fc82a756ef89a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d437545c840447bdae8913b007890414",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0e797962db94f7ab22e0d82067711be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80f7b8545076444facc671e0b3300bcc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ce4d5e116d742f181ce635293c734b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e4360202fa742a1af3623f6872306cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72dfa11372364ad9b8c980615bd7f60e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "938336f446fa4f88a048d454f9762e62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51cb06c6fa184b80a65b4109413297b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bf20a2e9e0d421197503c119d5d0561",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea8177b1561c41de9649075ed6e288e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c8cf8253f6f48619ffc51604513160d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa84b3e3b660438f82c7f6577ccf46cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da285ad784ec4292b9b6063a01cb4cd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0f01a154c0d43319638aede206887c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7a20aabb20e43879258b5bffc376847",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac7f523decf74931ab7417629206fe51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb22030e92ff4c0eb3d5ed0e1a027406",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a28c356a178c4874839e3d848af1d6e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff07bdf729fc4bc8a87b8f5e8bedea33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc089d70c62e4b6ca27cd74d1d29387f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b2eaecbce0e491784780a76e1ddebb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98e988bdef4946cfa8d6bd787d8e4c4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38cba825ef034d27a3c102daad8c15eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab0363002b7e42baad8ec4fbcb0fc5a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b4dee3118ce4240b48d9cc98f4ccf1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a274e97b6b8447138379157c3bffd48e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad166897858643bea44b8f883ad782f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bee2a87e74c64008b4ac8f9164d52220",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e3c6ee448e54cd59310893de40e7829",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fcf410d06d34733b1475ef8325376eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69a52df0989f4202a89ab4f5cbb86b37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9a843a37a2e418d86b78892d9ae5555",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec4b39b7ebc94e41a42af01bca468aed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52a45327aac6462dae96fd2326d9309b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5866e1762d004a61977014e13c50f301",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8042a4716a842a2bc20b4979688c2e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "734c55387a394845b82aab2315569e0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e36e5636b72f4123bb126ebd7d25dd8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "356a36dee61a4367a1d85ae4782f6d6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1200fa191fac4d31b6fd6c4504ba553b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c4b3e15383747b9b936e9f8f660c062",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "199ff9a23b0241b5882be96012fcf2f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff7365829eda4b74a59e8ce1fccbe6ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9981d9740a94e9eaed14ea841f1a1f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1323788018a4841b789567a9129bdb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fed4759744d4155b580552584be57a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "271b8cd28a5d426bb58593a094908d31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b9be0863e394a60915ad04d9efae9b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb27277f501f42faac5548d9be3800a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89eac6593858489aac65ca5a7b287953",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f464c52a2b645d2b6600d5d8ee5dbfb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33140b934b8e470da13bb75f1bd3e67a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6371c4865bea427cb8c4be18378bf5b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfea5490206b43949267af035ef91964",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ce6f39a9d484092a73efedab60fe5e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8dddc2e0142741b8bb023f79b86bbe02",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48b397892e3d4a2bb0f89d085c03710c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f55e8fafb2874cdb974e99dd9db687bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4deb42750d5e4eb1a8ddb732cb4ec4d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cda9980105644a3b94415b7878ea0b90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d53beda7ccb4b8187b2e7aea0cea003",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8350eab6f034ad4a6b48961fbb5738d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f71ad57ad7942009c52f10282b17e51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02aa0fd709504ac4bd49aa817c3e4846",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9116719c8ea4abe9672f212781b4b73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c09468119364404b7e06bd9c7fe1c9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac8867a592bb4a179de6c102c392c65c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "229d2722b1af44c88daa05c1cacd466c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5fd7e73e7de4d3aaef2c1a1ab33a6c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbd660959d934919afb7e6ad611a20f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edd4b26a772e4840af9fa1bdad4a8e6c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "589990d372b54883ae6440bb96c707b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0a44a98051c425abe564e5cd990b4a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f60ad37d7dfc4a05845d0c9113edabbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bec6fa0a299741a49480e1846e1a9049",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71cd53585bf6434c9d7e41460ae264e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bbf6d8a0fc4478e990eb7e794238e4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6791d34c32d44786bd7f34a2d8bbc51b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "465270bbcd324a4391e595cbde97b5cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbcf4de6007a4a9787a26b45e60a91c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17240f7dc53b42a49c1513cee6cd9ed0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cf1e56945ed411ba5837a2cbc858c77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6b7ace657a444eea862ba69e9978f47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b4487ba899846c98218e73fff50d28c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65f1afad398241deb9e22995fa8fadb6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9a1eb3a5f054c18b997e5fbc3bc42b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "586702542fda49ab9e8b2341a09a4795",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cb6af81a62449299bbdd05d4b5a2e5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a7f9bba87f0406ea018930670246aa2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17e61d32bc5e48abb7a302acc2f00311",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f01807fd03044cebcc6fd60d54e9eee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11dc6e9796f9419f9d01b9c7c0ce1163",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b5d29cfccc04f318c9fa2b0e57354dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b19971fda9084fb99a9f297cc5908280",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8dc3b784925439bb0a377099b2e5653",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f078fd92e3549fc9aa9f79a67f62ce7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86094799c80f45b09a22759f597c4418",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1cb0a2e80aec4a2b96dd16a40addcefc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc22d483df96488fb99a650f70b49531",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb270691aa1a4402bf6acbfe553e4ff7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d56250b98c41415ca74c10edafef056e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b1c91c0fdd24ac0a19cd766cc811e87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00f74d88428c482786c6946fde319829",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a8bdc0c31e64eebbcf9fc8d170d5dce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fbab1fc312d4c84910d1eee512e2f83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "811b1eb45f8a452d85c3cc7a27d0999a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e00146a6af24def9a2335694c9b0ea0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4039ea1307a4c8c872e4defb99cde05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18e223487e3541d59ecaf046bb5f5b6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7dde51f5486e4e20ad79e87080263b7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c060d4b2d114a988eed78c041e1b192",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd743a75541948ad960837df2e509d0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ede7f17f37243bd8690a479d35e4e93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09d2b8a88511465faf03ea583bffe2c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ca4e518576649a286aac7b776c32e80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ecbf4fc11a074d54bdeebc5c2a53acda",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5eea27c66b24dbb961f81fbc05df94c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e44efcae2c4449dcb45e46c15acf6d93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7b9e755f22f4e8e82c9ee10798e43d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f0302be0bff41f79e38d21f7cd74038",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0098e7414dd74c26bf49c4a2f39dbcdf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7da138f54d0541b5a31c6b2ca0cdec39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88b4096943eb431980a67f42d319d721",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b1d706909b64abfb044820eb6103fbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf8e48ecfaa34c4d8ee70c30e54825a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1462b3ac59f4bf39a4b63fbe4634b11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50887d360c274d1bacaf32666897678c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76c70d0c5f8f496b8d17966d0c2aeacd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bcc2fbd1f5214134b9bc9ae81601d8f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33a32fa58eda4fef89f25e810e5ee6ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fd9b860c1514234aded6fad92de6130",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2af5910d423d449cbdfd05ca1b31133b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad2d267a08294685ab857cacd0a02a26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "931a835faab34455bea8446b0022a215",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "691b9326bc3846fe866c39169c14ff32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e72d753c0c62409db0feb2824cc1c4e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cff658e505b149b48229f3731c0093c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69698592ea7d4da99314cf3ae59e3977",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17d03ce1047545d9b9c1cafed64cf0ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77f68737739e4051b533e0085f9c81af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec5529597f284a21aae4f8cf13034148",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb1260c6d76341e99cdd0a29a6e732d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce9b953f60124c66981b8a4bb10d2ad0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50e08b6326504477873d8da228ac385a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84bc9671ddb44a2eb4441ebd8d893413",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f66be20ccbc4188a3940121bc0f7516",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfebaeb049864aeabc9838e9594e6f44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "283d39b77c94413f96378bfd4679b2ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "695c697d70324b98b3af0e1e50ec37bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b42a7b2bf304166b7834830273fe8ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "433b8be0351a4e23af673528865d4963",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18c7f272f5ac41d2acb6b51e3a593e7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59169f59a14149c2b2cc4e71ab3d0a68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e67f3f4ece434d44b5678ebd7f8406a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "993472fc6d124a0cb49755a8d46012da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f1c74fbc46e47009b4c7d8c3571599b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e19162a1fff42e4b0c95c133f016555",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0e838d1947449b9b1471f9f66b303b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2193001f38e64eada00b29498284c58c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "958082db6aa349508af60da277fd6799",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed382d96137e48be904f2b5760b3958b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5002f6c5744844679f7def7807a7acb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c994fcd6ad9441db9f80b696f42792f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01fce7cb548b44a18ec8c5206f8241cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47c793cf77eb460b90ee256032ed219f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fc3407f6ba347ca9fa6671f55a4a336",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f75cd8f19de64a7a809575b4a2a81943",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b01f8eae322443ca9bb39a18ac378462",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba72eec966274c30bf5b00d0d659c5b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "866d1f16187443c79730d1947d6d7e0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ecfb8ea088a84696937c960b667a80ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ce3f4c729c341ddb2caeba2918ff4e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2ddea77b665497c9629c39ea6a9cd73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d65f9017a8d64f7e94cd2f89425dd270",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c8f97bc181347ff920b4866d47e5dba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7886c6b7f2d498fa02e670a159a05bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc93e2026882456eba9e913b615560ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec099962ce36424d98a213360d4567ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a2ed6dffb93443dac82a53c372cf048",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48911449634c4641a90c9bbbb09d7887",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afbca7f88e7147f7833434a2ab688760",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cee363e1a43d40a6a1e0d94cce5ae957",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45d2d94f46ce4c718171d5669d6e5076",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdf109fccb3141479d6f9386d4c5a3f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cda30ba9929462a83337b7533f52f12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb41d7e2013a491cb19e7dee10e00324",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c41311d39be4e27ada140cb4c968c87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "654cad3d8e8a4d60ad9ae3d413b2d17e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6958daa9f16f4bb5bb71fcede8fc7ac4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62b5e374a6f64494a1c43634300a284f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "042da2ac3895479e92743b4b049ef22a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fe9062a262b4c7ca436293855b33dcb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9999bd1026f4423b2bd2517850b5846",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd26d4cdd8854ec88f299cfa630ada42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35839ffcd1874cd3a0ca6eeb6950ac08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cce9530c35844cb79b5cf92cc215fa11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b722465ae506475c97e7cf4660ebc755",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e1f98c445da4e25b7d21db8e8035dd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f1d7095c09543eca99a49b0803c4356",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "439e7e2fbf11404c9a939d39a80c71cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eafd62d1ebe9435f830c003e9f8f77b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc748b7e9f9742bc8897474861e4f8e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe5031aa3d8a468990dda3114e8dc1f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88fa7f99f51c47bf944d8e709d9de002",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f49e6238b3a84ea2a370fc8bdbe90483",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f017d3aedc094cff8c493093a0697f2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4be91ff94f44fe49aadffdf7e52506a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4f358c44d3f479fbce9d78e20b6e3af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ef709dd67384463963a1fe9631ade17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d63f1536a9b74e1d89ed030f6379f2f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4349cc3f387844c8bb45a4809f7ec9c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55bee9de68b648238c0af803f2d0c502",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aee92dc1ade04f46b65a877c5f4216a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d51a676240784494801fa9281e53b34c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fecdfbc837744cc97058ce172cd31a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bc563aa316640bfb972438ec7d3f488",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d1f86af90a64742a6e5577c3a2918c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31c4690e310b44739e48b0cb16091446",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3368c48fbfd54037a043226a717d58f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "777ab580c0914cea8eb0a7044c414916",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1cf529338a504051ba22970a50e3f0b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7716ee8c3da24d70b961987ef868a333",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c46dff8145f4a21b9a047f45a340545",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1df86dce1f44871a7c9ddb8615214bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1a2d8b6a21f4495b88f243b7ab44e12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee99246efd724c7aa14558059432725f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "475e1c7eb0894e3980d71435e88a0b4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7e3ca494b394ec9bde3f6f56a78a81e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bae808ee3243458583c53e24f4e85e05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f112c654cea24cfaa324405f8dc69895",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e20508c6d2a46d6998ec125f640a805",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0bde6e708a543cbbec0f84a8656f97e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b632142fa9384099ae467db16cb79fc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ec67be64f5d41e5a8e6e7349958ca7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "926503f41f3644d7a852f412b3957798",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "08928af0ed3246b89056ee843b0a4355",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fd647a4df554974b4f621b5e0c8543c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbd2364714b2490bab9ca80657c1cd4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d277687c6b542f0a06446635930b975",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "584b6fc92712467bb09c22dd27e72075",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1166936f60ef4434815bfb7fa9a63abe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa63d0a73ca84e428bfa89743605b775",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec8acc5ed1f04401aa05244b09ee8666",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d0bdb641c7f46ee9f37948615acd7e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aea4e69d9cdb41e9b4e2dcc4a556eec6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17534cf72fa14cba8fdd0373c7674eb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62351f9a2d3a498e8587c996a19c7740",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7919fd22497405088faa52b8385079f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26c2e06dd0ae4226b4b70745340be431",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "baf4deba0d724a2a84e65ffacfe77fe1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b63c5b10e734638b507255a74c85efb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7805498133c4216b2ee5a875ddae480",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3eb0788fdbeb4a9e839d1df291a688eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5eb397faae934c8b97d009ab7494cdc8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b9db779a52244e688481c117ef42df8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b3ea649f94a4f5fbee32df73f65de53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f66fbc6a0064d6e846f464318b771b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "407ecd142f004e8fb642084151a1308e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6eed4739d63c4c85a31cc0cfffbd9f9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10ba66a312b14f44a0ecc55f165dc08d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a930bc17100f46f9b7e6f0b7a397847d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ceb37f6a321940599f1b68c0a4192b8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a68d6c53d46d43a6ba665fa9899b0596",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93d21bd1ae4345548a98bebfc852e521",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c65ffccc3314c91920cf92964d969f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a90e4781e484b8db9c0d1140f7360a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94f89eb1f6c24cc894e0f537b670772d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4dd58e0946474a549120140f5ed9deb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "051354119bee4f61826ad35de0be4e11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "850749c99bf641b0816436ae085bfcde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b009121a22f640888848052517d93165",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76d16714e5a449f0940824eb8d978f70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4790cc471a7e49f7a3aebbdc65fe2736",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "045fc4ed552c4c98b0a025e130befe8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f3790d1f567478d9a869724a6979213",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d308326d408c46ccb55a0413aa621d12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "44f56ce6d8a347089841ff8aff98a84f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ac2dd4cfb30410d932e7fb4eed1b6d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8711b367874840129ca7524c847479a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38da8d37fbba46d181f572c45d8752d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2037f7331d3e44a7b1bfc3f04157edc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "401a062a33ce4ba1b6b6ab01dc9898a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "243516f8e4354a129ef983f2a6de36e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8031ca42d0a46fdac8dd8cc5d541c48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba1a1db7187e4b76b9df9f1d84843925",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b696072c2434663bcdd7d02b57a9799",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dde8d857b4b140038fe21910511e7a93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d5b14c0056146ed99a77506b4ee602b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aff55830430442deb85253cdfb292204",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7b4cfb721f14105bb11b27d048ea1be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65dcc79f064441f7bfdabd59a1e29844",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4714bcd722274473aaa6c058d2ce17ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a42699e00694273aecfb3135e07d41b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f4b4236a5454bf0b4a95d3ecb9dcb41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2805eaa34ce5407c831081dc480eac2f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f5674b00d01416bafdd52baa62aadcf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1621b0184d214b96ba4cafee3635cd2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b37648109a614286aa55918048517b2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "08050b2cb5614efcba4fd638c1a79759",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5898a5d9e1148e89d50a49fd755ac76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4d8d282f40942fca8b939efa80d574e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "723be86adcf84805b98ee4bada30fbbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23d7b1aaeb354bf3978fdf89c8850bb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25786051198f40ab8226d9fd1def84e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09b3fc21724e4d0f9e4faa522896560f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cf0521bd044407fb131d9fed92de3a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18d818c15c494a13b026db9b2bf07309",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b26370761e240d08bebbe0f043f876f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce47c060c78b4d3a81c71f491888b721",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95ea830570734cc4970a5f6a37c0195f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c45a1b5b44cb4975852e927557365376",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07c6a1309205472e9cb63decb8573859",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2fcc7889be24a50b37a0a5695c0e753",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63f098946ea44d8099f92ab8096dee1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f56e142ff2c47acb03bd3445f170292",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "842d5e52818a45acbbc073ec97103cb6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aaa54c0a64f44f0eb0c1f90f00f3915e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9464343c0dfd4c689383b4fe5ed5039c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b356179806d491c87fff9ad3e86b31f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09a4aa60a12a4c5eb1eecd2966ebd98d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d57c2d7214d41e08e87127e71b21a04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6dcbac29418a4faa88f57d82a530d0ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9d7b06ab57242e5a422207528586c32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8e422f66b8b4a9a9038671bd31f458e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52044780c9bd45a8a5987051ab172830",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "accbd7ea5fa5459983f98cb246899fca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10bad9d741394b389ab1a138f2f830f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47107f7c5b5e457f8bbb8a3e7d2da5dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7ca307ad429462eb3b400ca2cb99226",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bdd03e57a284ef0879d730e2e8c6b6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3233e5bae2054509908b330686ae1285",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce89edc8dfd54e198b49c60c17ffdebc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4dec6562a3b4420d947d71b01927fe08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c834509262249b1a809cc8d387ee6d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c672f782bcf0452299da107ac7970973",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4991f24a857441fb43f32625400289a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18a17320b3ab44d58eddaef3ac20b0e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9af776da11644d1a03de46bb744527a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bd841c1527f4b3d9f8861baf94b2c9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5512e3fc44f41349e90ef41f0731020",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "669dbe446b9640d0a159130ffdef8ac1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31edb8861e53493e9b4200e2b7c7d1b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25d4030f3f30476daa322c58bd0bef34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33ef4613d41242fea4948bea7f2a690c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb0c55c5a11d4dbcb8c1bf697309f394",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "266aa64159d64c3296614f4cc97b66f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "254470b09b214184ae9702a4fdd46de0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "249299706df5449282c92eaa2187e626",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85ad341d46e648c8aa9e0301dfc68c45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63eb06c46fbf47b2b8abd6b5b988b876",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8a39123b06c424f9fb9f3a87b37e4ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8aa946f1d5444c499060baec5abedfc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfca07d5bf8a40daada66854efcd137f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73cffbbb8fd0422db7fc1abbbabf9f26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73d3b5deb8d3432287036bc2042ed778",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fbcd12d62d34a66bb18e66cc931b34c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f63657ce2bd4bd59b3da09d2d14bfc9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b59f5cf042f4e7fb782a5e04e5946d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bf7c3083c0e4b4d89f4716946987f72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "288d5a5a290c4fcbb21b13b8ccc4242f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89c4cc2f1fc746ffb579be3432a2802b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "232e04d7c9314d2fab86ad4e8359eee5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a677263640f4120a676344b62160219",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3491d694a7ca4052b5302800df03626f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3465e6bf0e2e4d95a5cee2fa992b83d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7691181bc7904ebdae7b71f7b486cf72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7d721a23c834947ac986464b84e6bb6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0ffbd28ec0b48b581d042f1afe6d794",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ab54e34e81541789c72d2a8ae35b4a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95456a2e12704287a2b55fe1ceb19b20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3e88f41c9414c4ea077ed28ec4755f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59928c5e8517493f9afca47838e69280",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a2ac78002984f7aa30a0f9ad06dd96d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28a4f528ccc94209ace9238a06f693a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "052f2aaa58f04514b439b1aefd2e6bb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6ccc959707244c6894b4d30d28b728f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2724a60ce8564da2bc7c91b463e34ea8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff0f58d91da24d968c3eb16c4c5c4077",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d2a5a6d117d4beab0ec7daed4f5300b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7dcefbac8714164810ee45348a7dfda",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8befb96bcdf40639b831808651e1135",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6fb5bfb5e184dff9a298fbc191e662f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01e583db9c104a719e62d9a9461b279e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49989982dbec4d2099a6f68645fbec8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "548597f1b4344e838920838c66aab564",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f52911b05c345dbb28ff1919c73b6d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86e54eeedba542d6910afce80c7a4894",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7eb8d6783ab34c098c8544712f4e86cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9d5fd3e53ce4cc589a275c1746317bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5693a2ddc10465fa7727949f46230f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f138be48abd4e5e886456c5a2ab52c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7ae05ff64784e25adadcd069fb20507",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9fbe4e43e544170a88bf0f8f707ac05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d63cf2c9d94465fad115f2785578140",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "337632a3e26d457b8c129bf8cb19b118",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d0cd0aa56424eecbd62ea9589e9d227",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bcdb10958af24d59985cbf8c6e23e965",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3733d9e8cf104bd88238981804f83185",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b603b24bc9294c429c575017a866b3d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72335d4bd5a04f1fa581dbe0203b2d07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c425ec06767a455eb733f068c498119e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d572ae9ccb149a89996b539bea6b585",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2645cd0dda1d4325a1ec19e438431b54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "221917d7b6434e4c81b403f10f9fae41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10d662c626af4cf39921e1b1616582a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d913a4c8c20436fa72d0296714fa269",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbcae305e4f74dfda8ee1bfd35026122",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3431130d22344ee99d92f83dc77dfd1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a02ddce12fd04c0c8000c6fd5b0f9145",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0c1b39985bd4ac09a14b7998017d8f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6dc65b343de041b191e25a8461704d6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e97998eb01924661861ed21e4225d44f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a98a3d38b24441efa1a5fce663259a3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e294dc281b4474282f211f0159f5318",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d8f798556f94b3bb1f50bbb138d245b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "798deb0ed77f456c86a338da51621183",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd5b5dbbef2b428f9c6333b839f80718",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc43de998a3c414f8ea480675902760c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d0afe77eff0469c8c96b108f737458b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc31593efcdf42919567ddb550735add",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fe9d32fdb254f5cb028de1c04cc5246",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3774050ca8ea48298bda982989a077c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7f79072af474abdb5962ccb1e08bf3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7de67293a4364ed7a0f47a9907185e6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e62b0d0de064d918ec21554b07f42e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4dd18906bca45d4a6d2286cfdf6f13e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a536ae11804405197c4609153cd1ed5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6174c6acbcc14a2bbf0393029641b59c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bed97d3992d34e42a70e916cfdd633c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da417221d7c5420c98c7d4a7ec67f296",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23cc1ecc2b534defbf8e9bd546947e56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47146326971c411096f26199b76eded4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd0036d744644116888331e6077b1513",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a89953665a2c4da39bceee059f26c039",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48beb0e27f38461a8a386dbc7bdc5a40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "975a1c80f7524216b27068084784dfe1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c8a6067c9f846c2aa814f69c8cbc6ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b76cf8ae3cb46f09f1920b20cd46586",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12831b6934b44e679bf12a1d681636f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87bddd76cd5e4791bdd2907767779e71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7020de62be5a4cbe95075c39885c1f27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "892fab3f8aa24085ac093330e37e6747",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35555a5e20f841b7a2ae0715baea6ee3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e272e51e95454ae5b8c3b90a04070498",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb4f4329ab4140f5b46afe3af4fac392",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85b9edd8c2bf49b4acf7b2839ba93a68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06b15226f427468facafa2590abcc3c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94f3d46610ea445f84f9a86f855efe35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9d8bbc4e7544965b57e7e7f8f533a67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "247fdfbf9ef1472aa229d3a413d37d9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f217fa3fad2478b9ca317605a00a143",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40c4ac7c955048119033a85175b08cd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23a0a261243341c881268890a213519b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00b568da5fd34c0ba53d8282123b2332",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3224477e54bb4fd8a54794726b5a0cc0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "065a367aa7884c4c9751ca908541bba5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83de471a36f34062bd5a6a2c6be222aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "699ba930a1094d2dbbaec1e4d0d0bc1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c720013f1ea453fb87e4ecfdca88b07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ddf06da80f6841ab86d6c2347a63630c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70ffdefe27c34c5e90a836caed9ab724",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "263c0b1cbb5e49b6b4f522963d8d9200",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8992f215d0d74469879cca621b1adeb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2195c3ce15b42c2bd73d8e4447a0792",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c8f54cff94547fea928744139d96e34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cc87bee3ed646c3a32995afbaa90d98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5c2bc2ab23f48139342b8a7a5def6d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "139c91ed91d34918b217648be40e9eb6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4595837c47da41da939febe68a19ada6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "883908578aa94b19a3c02473ccf0ab2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7938e0ea3244db5aefe0655ffbc4475",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f025bdd3306849caa9903f7921489ac2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "960b71a4da4343a18ac9978c6d1544a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a8e0f238c174b118c67c8653433e5cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bee8a86c0eda47b4b48ddfeb8f68519a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1872a853898e4fba8fb4dff549969915",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "195ec5c371924f50a61449b3a595d450",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "727da3ab05e942daa28aeb268bef6743",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09f50138401740f598d0b8aa3a3f0913",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e119948a5f0454ab7a10e115af42178",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce8ecf38ffaa4b3f886ffee5e041d24e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fb4cd365ec742dfb9b464a4bf63b4a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f92d12318584fabaa3fd0fa852440dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "befc8a003ffd47c18a5ab436f9ec7536",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1320a3c5de59469ab8cb2f391e59bba2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf97e89a173f4bcb850d1d2f974bc2d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b3d80a010cd4f44bfa51d5afeb4bba8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "539c371e30f148c9be0043e17168ad85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d228a124adf4ae7ab4f5b6b69f62f5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2ed84a6064d4698a939afd78c0fc119",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ae0fcec1995441aa910f2d8eb9316fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1f823d9eb5f4b8f9e64e2058a3ea07f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1458e9e65934c7abe13b5bb4a7e819c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1bf907060bcf41158a66d01cad5c5c80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "536bb62027b44dec86ce66c5761f2a35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83c1831be5164bfebabda68f9362e147",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53b8419f2b5d415a89089f9642998b96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29ad725d8d1e46baacf8b286612cb764",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6b676f0d5774b3f94db077272933979",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38614c7e257d4d5296a34ff003887bfc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9db296c97eae4b12835afe937ac78dec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e30a3ad565b84eaeb9bd0546eb7bd51e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6207d07f37db438eb5ca44703b47c059",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "766cb6cad00842ff9f085daa33847b1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56de6324d122496a843dac7b89651a9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2a49aa8c37a4bbc93d483b9be930618",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "737a486dabcc4c00899d9189e1bdb3a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "378414e68d114379889efb35467bbf5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fb31d5e6cb0428286668fd24f24fd73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53a559a966ea4b02a236bab4eae167c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d41a214d35ec4bd2833a0101121fc1b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb9dd69fc8cc4bdbaf9c86eebd0b54cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "169145ee6feb4ea5adbb20e844aebe94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb61f6f9a0a4421b9d954b5f7cd3dba6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49badb0fad36402fbf03e02727ef2241",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc8a2f27b7f043a5b3540e7143d75b83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "712c3df40b7f4bac82bc3e95bb4052bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3eee37ca40f468495b674a77a7d67f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fa00b6133f74cbe985c3a118287c788",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76632608b6ee4f94b6861a22e2cc2957",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5404db11f2745158a26daca8d7d7d7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d675fbd92cd40958cba6e5c2064de79",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a49306b8ce934d5191ff7c981f0a8faf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b9ee99043a64a0ea8945dae5d898f36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fae148b686dc4ce5ab0eb3b8d8616976",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fc53090fdc746bf84165383ee66d121",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cef518832b24d1b8b45a77264808fcf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80c2308f05e74cbf9bbf61dd92e9ffa2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb7ec2122653466e9e2f5874ec4aa0ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e043ea2c79745e89aa99e612e02f09e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33676c0929d04b79ad28bf5eabfc10bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb229a785caa4addb7811220ce899ba1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82b38e86377b40b385f8c8ca1789d26b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b44953a9c58438d8bd6f7b1a3797b91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3e47f6ad9634292abf7d0ebd774dedc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d186f6332ac048659d641f18527b9d18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1647d0324ff94875bada0f7c4def9aa1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8268df910fd4bcd9345a1b1b0882e66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3841a43ee14f4c019a11ccfa7c28974b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acc9ea125e9a4dbfa64ee3c1e1fc38fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9da57aba8744600836f28830f284438",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72f04e5071684add9eb5b059bf22df74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5659ac4e072a42ef84d75a2ce5c00286",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19bc6b598ba94016adba33dd2f8c009e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "512d7df151384fabbb98e501748084a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff09173fb42b43e39d83f6bc810839ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4c438ccdb5d4b6995a898ffa42d0fdf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f97692067c9b4bdcad0725f9968580e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f41cb412e0ee4cb88105fedacaad753f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ff407da614b4e54a04b88ef394f2287",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "515c2993eed241ec8d7fdcf74ab7e783",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2c223ba4575427184deda9fd5d85965",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4077f576c756403d9dcb6f94134d8c76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf0566f4689746658f28773f003a016a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b64adc9c62e34af2b1782d96ca25b89f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ed8906d5fa34df19d1067111a203283",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8940a34765f48759992e1440d3c6378",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22cfa86a5b2c4195bcbb6e8645703e9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "470602cfdd6446c9b0dcab61d8ab937d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7dd0067d5bbb49749431c5ba0e358a2d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d966a176adff491eb6da796fd1e1ccbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d31081833da6491fa53f26fd90d53afd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "481a33d2b71f4710a44a2c172baba9e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d168e597e8f14452820aacbfd27f7ef7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "863a12379dc94f7a9d1f4f3ed2e5f141",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a6eaf077b4944dbaae783fb191a33a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "907f0898d3d642e78a29d9628966a38a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbc37f88de0f4367aa0f106387264b73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8aa2c6f99ba64a0cb9ec00561472d652",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97e6c678433846638d84b5ef86737e50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fed691bb52945bba0be4f3061140fe2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c78c509efdbd4135b89dc7430d00bcef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f1580fb368d4c81adf1847b03762494",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b8cc8435b444722a7a033a7465c1147",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6044aafe8673450eb16c0861104004d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fd0d2ab21214656824e2bf1e11a9218",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9a33f706aff4874b161a267ca69ed97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f38067a7275b40b88b690fe7c4470ebf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abfb98782ad44bf39ad1fc021ac6ea4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c92d2afe178d48308919779c01510d11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "640ad907addb479bbf27064fd0b6da3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8c6d0a427c54879abba9bd3a10b73fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea7592bf5ff94ae7b72b398aa8ca1f63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e304b2a638084be2a1c5d7dfc34dc021",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01d43980decf415a8b15011a0017a8f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6350dcbcb82645d981113be8c4c7c9f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b7db4abfa5b499fab68de8520f38196",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c45811662e7148d8b0adf9dbb3bd84a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c577b06ad9448a485d91daaac2a7445",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a7b736ca52941f6a29e0557fcab4d7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa4bd63e23674d84a19467528f3e0e3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ff0b09257344f3bb354191b968fba68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1dfefa957f3a4842bd696ce955bb99ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11ec39dad7324d04942f23f7b48652b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca8939dd6d554b62b46ecdfe51acd74c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9292cb1dbf24553b083a4a38cdf6574",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a951f1d9659543efad07ff5241f2e6a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6f2d2233a9c42278d85808478e3d561",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbf03be830ac48ef90d57476e70c1bc2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "603d56301534471f9efa77bddc003a9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2d652102e194a9caa9ba61d8289d6bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1093a2a32304abe95645674c9469219",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7384473152a74703b97599e1accfb156",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a82aee82ef34cd9b078e29c08df1367",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3cb671131ab44ad8c0fb0ea36b59d0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26e54201e0924797afa726bd64697304",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe90964832e144cb9ce95a8355bb04c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "caad115ec438444098b580a36851e071",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75a421b48edc49cbac9ad33ae1a726d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4bfb94803b6845cc8cfc082417ac3df8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e76bbaee87d4124b03aa7e043764eb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a68c3869ff7a4a7d9eb0b3ac23bb345b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31288cf7d6264eef884eb06cc4e5fb19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16c15289125746d796120d222e1cd818",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa434ecbc35d44b69b65b7796874c92f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c02a76428b3f4afbb79c1b98399b566a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c84cb50a76894e8b9e6c01d57be334e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "619f00dd6b3d420a9b248b32df939ad3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4cad283d0484c99b41252218be4fc11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf47edca4e944e40832cb81ffae349b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2a1378247134ad8a65ada336c5d6b17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e740a2cb1180424499558333654a0273",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbde4411aca04fe3835e1f46bf54bf92",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c9b8d3de41e4ca8b206939b0e663084",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "862d81a652d546d0bfdcb18d44e72713",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e6a1e114a844d88a1306413f9a636f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "248ee6ea8517445fa22bbf74cfea25fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c6a44e57b4a4e7fa78d3eb57c63417f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5c15e5a93e04e3ebb9fcef0ce9f6f51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c3bc4fc582d4a41bab30de2fd491df5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "57145a91136b4025a915770cbff16f57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a8ce43114f0477a98349e5e830e806e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11e784c603a748b7ac181dc611cdb08b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb9e4802ba294ec3a622e2772c6244de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9b675224f7948edb2f245480a3d3b18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72c4adb3234047e8a4baf01cc5fe289f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b47f7359bcb74df2ba98ff2b2dc87bb2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74da8a810e96459b8123d6c10e68ebb8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97d911708499467c978d0c3c07534f2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f113f6f370644d4bbd6002f824ffadd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "676c276c915d41ca8645006b65b6129d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29be5a8431ff431e93ba3cbea1fd6bdc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05bfb52e05ab42babeff9ba26cfddd2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8964d817ab244fd4aa44954fc7af9cb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d57ea93943114902aeb03b16b3f0c483",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23eae242d42b4ef8949ef717912cc986",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4dc5ea211bb4ac9b9c353ba834e5f9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbab1d79079d455584ccf8f7d8416412",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "805398897a5f47afb5bf140d02bd4145",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "698a247f334e4e91b1cfc433a9a9bc0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e6eef18887f4ba688c9abdb1ca394b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38c7e30f893d4ccbb13fa628ec05414a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1dc0908cf0494b7cb148a40c06d59df6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02d81d83fdb44593a392fa3561470b46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f784b873e49b42fd97d63c9e4b0bb61d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fcc6a0d7db34944af8fde90a4cfa70a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89ba0d071ec3421b952c31fda7cc5a70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2148a66365d4a0c98cc9e36d798dd3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "212215208c2740ba9708dafd764639c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "484741c50fc44823b9c34f93870d8473",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b60733855c44f93a395b39be24ebddf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "550033288ee24f0a9f734481504d8c9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a2ed6786f094c0cabbab7630d76de3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "219ef8e126044b70a70a7c19e5d8f5f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "988d5965e6be467d84ab6bf6a54ff35a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d0a02178f0146058b985321cfa0989e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02c82525107045e4867c66924921f223",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "356e4bc25ba441908eeba244eadcad43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97316bc3a75649de8dd1bbf7a534f922",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d7b262bd7f6410882b3def07b5526c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf453419b65740db83b381b18c83886f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa7afb55b86f4dea813b0a9719a1d2f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6a1a70e16d349dfb5c2bca25b5d2460",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1885df35e78e451095f183b74fd88ffc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0a133d072174a579adce38f3d6b02bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df2c20e514564d65a9dd5570ca465301",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "739c74e73f6645e4a70c8ad3955a298b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "940e7ad1f6ee47bca7193a305bf92a87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bab5d05a61a54b3ebd3b0e8d296fc68b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd58823b809044f3b5f802a484d819d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7440466b78545aea3b18c0372c165c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db8a3d0f47b043caab521594ee231b72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e41631d7b78043d5b08be3ebe51bacc0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2078e77d515746a7858ba11863702976",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9162039d5fcd4acabd29a5e54781f745",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "944af4f567dd4e518fd1d72b791a924a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cb9bd2b8aea415b93403cf3deed388e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e5a3d8e03d84d15b42223cb6a7676e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ee8c37565fd4e859e399cbe6ae32a06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf25b086bdc4437f8149d2b24f75d7ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "442f2730b6cb4e54adf14fe80ccbb268",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "066f8d25eb794f5d90d4cf86f92bcc54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2f59727b94d41b392a0365fb1574012",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66a1e8f875f6450985c7eba1ca805a53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01330a49c9014e39aaf77c7da25f30db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26d36af1299d427f923b9e9ba8024a1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b0c101e565d48cf9325f5d1ccedcdcc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a46d0044fdbc4303b0103a828dfa91b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d1932711a0f4f4db20252e96c3119bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11cd982a76f3412bab60da556796113f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b84b02cbe8b049b2b85de9ada99b5468",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90600920247c453b836f14d76d9a8369",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9ae369980484664afc81ae7272404c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00fcfb264b474b38b661cb88393c4ccf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "581950d5977a486c8159c9b8a6b9cc3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6e9715a47fc4fecbf85c347dfea6800",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d383d26999b94205a5c238324feb5d2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "251b9f22556f4adfb7c37ce4dd09a9c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d93c8c5b86b7403ba5f5491d1d8e5a64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00481c09d48b499d8dad43aa9f76bcb7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e749eca98cc4e5f84bbadf892fb0a8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "557ecde661f443dc8cc1065143396e59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72d2a648828f4be69c1c638061d1d342",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5dcd2451a43a4ab6bd2937498e8f3d85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6433960481284eaebc54d78e4d1b0a15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02156b8d7142485bafef03181465b347",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4491fe591dbc41ce903dcd5e5f8ae474",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36e081f1c3fa4136b63a24d78e52cc8c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a80799d3f534a9a85cd6597857c975e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "470e852e183241b19cebb2c88f3bcda7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01f2c9139df44fcaadd66b9495a817ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f4c4434b5c84db486e968e7e440c813",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "448c878f365d405cb014f8317f7df11e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4727e8b291a4d36932c7018b1ca7585",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d2ee18942484ff4b9ae9e770296a22a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f08b0becef4d4a658c34cee06850805a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "668822b61276408a9b39f66530822e45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a764107820a41bca9e5f3a95029db18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c49157d22c454d91bda6170b21530ddb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fb67bb603f547ccb6c395ae4fa294dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cc086e773164c378ccb9f5f00ca9e77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c9097b683734f859b41d2ae387d8245",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c4dc6f82773495486c42224718b4b9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "679872b3970f4538835e43afecb61445",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3054d08a65564c2c907294fe2dce3a25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "480aab695c71471e982abb90a4586870",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99bea15582e34becb426663ec693c4e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "382cb2dc9d0d4964b456128df7aa73bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64bfd8f961944c7595004d106e399dd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "635cb4e141c34962a30845dbaea4a7b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66752b63047041e28fbdc50dd8f4d608",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6195c601402841b0b31c0c2da3827673",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f56400de5904a1295929245e604c38f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f0c78923cbc42a5bc25e095d98e5b26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "610f4590cd494b30849daba0a3adbf37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ddf604013ef456096240c6f4a9eef1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "554f7381069647609014a82a63415ac7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3dab74422264affbec092ddb889fba1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19d4f510bc7c4c8f9d0b6bb817889e8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2acf7bee50404a2aadf98cf5939085be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4b231a921af4471acf8bbafabf6f17b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "479e22513dc441c4812cd23bef74f05e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f06fd6d3e2f74bf6a1ccd3654766b1d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31c9383cd03d4c0a864d78322f513012",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c67c9110877f408989017e7199fefbed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4dd65b469e3d494f9ddcd927b497916f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "479a567057d0451abf8272e0be82b4f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c6d93f8e9b64c8b8614ae0878bec654",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04033b85cec14900873ad8672c4c8d39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca60e17151e2477cab9ff90b7af5e835",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c93ec85cd41a49b58e3a242b515d4d40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ee9703ee3ca4a5aa1f857dee8ba58ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af203a36fbc54e1e952e7a69722f00b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6d412266f35450eb7dfe6a8bd94d897",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbbca41a993b41289d34f4da118bfeef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc1da6660c2d4029abd399834f9a33ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef1fdf6a788748e98cda11e08d1e86b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cea2ec7552fd4bbca95e3d4e91f1d931",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e195b63633f4008958e277862e3110a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca466aa9318c42caa86a7bf3b2b069d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f1ca2c5bbb7472c9140e61f6ce262da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cf15cf9c11f41b5a55ab3566e486ee3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e67087da18f499293acb96e82684f6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "162b19b11f8d459cb5520788098a8f63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "facab3472617491382a6536a9d846651",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88c3f83427554cd990f60db7fbfcaad7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7dbff68b6506496290c9a2b315c20b87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c94c5073a11e4843985058d372a261b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0027e45971b247a08d9708e8a4bbd963",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c05a8b0e31c4d508ab803f43b484d6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d10b7042ce6e405cbbed1507375dc8aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77419abd9011463b8071068e5033c591",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8a800c404804eeb910dd122ea36f129",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8324b33f17044e7a82dd47b45dc0bcab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcd1cf626c044dbfabea39e2c3581267",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d44d23d14d60417b89df9ce08487ec16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d3b789d1be6486e836f5527b3219333",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9434e3a6558a4a519fb43367eaf6397a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80622734f085440fa2defd0e263b30a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a0089eb75cf40b4b4ad1e948cd788c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c131dd1536a64bada9802ac90d6b617d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7c1c9a4b9754b45afba2c4c39de45c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b34a4edbf79045e8a3758b974d659333",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd9acbe0f9aa4cf4bc65b1689e1cf5c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83002506b5064c9a8cbbba64777b5b95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7dbfaa8d46548a89e227570d49e9f10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc157bec485a46af9a85fc30743847d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "187c7331df19405ba2d6b67271f2b17c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11503ce43abb4a7294ca125295721cd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "623d041418854b4fb5fca9ac80fc0464",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79cd0d1391d840bdb09159bd05352a9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b868595c74fb4e04a8d6c371b1854f2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e383e52ea9dd4ec5b72ab33b52200033",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c2e2bd53574494194fa644e8478db32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34f05e21329f45c9a03065c04ed0e0bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c773714fb6044131a2d2c127a0dfdbf9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58e6badd02f04bd8bd96a0b5828e3f36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33d6506a03454954b99b2b79881e5ea1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9df9b4db70704b7c9d49736b60ab3306",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed48728c817743919ccee84ec27a048b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ae917e3b0f94fd5afed6c0d3ef2863a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca5c5890e54b42c2bc8981fbf15d32ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48174d6dba7440cbad830bc9ba6372cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab61a730c76346c9bb4131c2acb91475",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17ce3f1909d34d41802cec2ae8165439",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c88fd80c57ad492c84979d4e5578cc34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a8e3f02186f4247a4488a9211ab4359",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88929cb4eb934e1a8b5aabdb229a2d82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c45cc43734a4237bbfaa73739b49928",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de74460fcbab46728c791f384f4d599e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50b5509e75fd4cf884c67b9f38eafe62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76697a7d7a9d4eff93f8744ab219f638",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3e788602c0f427388f53d8fc0782ba3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "277febc97fe941018545906a2e5046fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72dee163a9df4bd6b8e14a159d4c874f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cb947a6697f4f24a106b2966bd7fea4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2471e1ef6704786ae182ed5be466932",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4001c076d106469ebd61372803a2b6d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "666cf6dd05944cecab80701110e36d60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f4a852ab6204ef1bc219ec3d5d8838c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56f90ffa9cda4d4c90757b8f091c4aee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "100f25e7c16343ada1e1311ae7975a60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50392da97d1a4644828b7002dcb57777",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc774cfc06884f44b976eeb7f5802771",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b1f0d35e20846c88ef54c2fc11baa3a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0860ac3d638a4846ae41b780ec1d9ec3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2b03aff3b564182a8448739364ca665",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e82a3a710294531880a95a5748a0a37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f481a3b8bd15458d9041584e3044c698",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93c2ec669143411a807c92f7ab5462c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47b8a14bcf70413abbe43f7627aa27f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3dbc5f5b7314682ac480e09356098cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b0bb7499ec443d6a2aa76f371e9fe9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d3fd1053d9c43859270e763bfae280d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63dea8a77dec4134aa5ae515f4fcfffa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18898c318d114bc79581df0d9a0f3da3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e52361bdcad439592c4c0e7025d35a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "280230bfdccb4e2e87a98a420fa6b0cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50ae931e7faa4f48a472302501fb5804",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72523a7bb0d843098873ebeb8cd3a93d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a8cf77fe14249ae8269c40b16552b4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db0d8fd864824f39a7d30179401c694d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "526bcd425cf1485591b2ceb28436d0a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77aec026d1074458905b833d98ed8b37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6f9179eb8ab4432a05a0bf6bacf1bbe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52ddc1f297034d6a9e776e8fcc2445a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f388bcf6195b4019a2cd8825a8d66a61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd93d86b1b584da983cb90ca157b2ad9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e098157c1eb840a7a26dc4ae75e3ebf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "474a5d3524c54ea8a5129c643bd49469",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1705b3c586e34d7080f3cfbc14cf89b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6172f4cf55a044c78c292d7155b7a4b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd5aaea516a14473a18b7a2ab0e934d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f2655cea63a4aba90b1818764163b40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbe8995d8ccc4ad6b62a4b58d0427d20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a31c35b69384996b08d09c43ea8d7d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8f8ae092b0843329ef1d4c769fec049",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9afb4dddb23b48248f8b1a60ad91360f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05aed207884d4ec4b01f785963a0ab72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8594852d3dbe4b23bc8195b7880453e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9056153c792949058e543a76b57208f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7cd967a4ba6743eeb4afa4f8a8341105",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "190c8b57f7f24c34911e425400659a18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74fee1fa4b184e519b6f4c934ae9be4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68bf83ec004e48c5a94d2b32352c2af2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8c2b394218241c898cccec706495987",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95c38f3bf5fd4e4885d467fe3be8235e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3ebb5fc64c944e096c6716b6412ffe1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6717bad049342d9bd746c8c3bb631ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "960436d517e64b949ea8590c975df02e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23823051ae474262b59a510541312d0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fc3df5ff2184718a6af4777b4959069",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37efc074669b485e932a48c5eb1e2655",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03295dd14047468fbbca6cb4becb5e12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4769155f7ba4972bbb8324440000fd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "774eede6b2f74810929c97c8b4da4e99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fdd6ad618744a8081895ccf3950bc6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc899f040e81415ba3e3e4ae9fd91557",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "825985bd90b6457dbd0d23b5373b2c7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf2a0b97c77a4591ba0f436f88e54315",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "942add8bfdd84851a390604fa7aee66d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "421836b8c4204543b2cefaa8156b8bf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9452a6f4bf34b5b8be042659466ce47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a225fc4d5bee45f7adb8933291e6b6c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b86223a73a394d4b982f708b406e5e10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f42da99322674184ad0736cd76ffc45a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97f3e6cfdf624a0d9f74195fa46a003a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90679c234c3a4a709d33e02ba38171cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a385501ba8a34cde9ae57c1b73b8928a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fee7920a65b4a728a471f00dfeb90c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28429484337e4132abc21246ebf6b183",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf376c19c1fa4598ac8940d7d8d08dce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e13f6cb95c5f4f2486df02892375d47d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1b0f27693c948d09966744c1c28e001",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63d55077c92147faacdc666fb1d6d3e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acfa5add62cd424bb6ebc0590f438e8c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75d2e13b51de4c0fac4147fc25a9519a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f29fbde624f421bada585afb6e0dd23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c77ea07d255545b09c87b940b5c4639d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37c132962fe84921a742f2651d52fece",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b0929b07cbf487dab42d01b18b1a223",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1359b1c8744140519f37b248087477eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1584431d48743d8adf9fd4ced0009d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a55e58b692064a7ab6f7b04cf48e2b34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c221716645f04c3aa8641890f2f2de13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b456a516aa4b444ea33e469ca8562d54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d7ca6cbe3694b8db47d9ae9740b62c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "755676bc192e438f97e5ffa68c5eb56a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53d262c893994770a0a9392d490cb673",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d14d561d8d24320a297fa4c241a8642",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4a88e62245244a9b31c5fcc8d7eb1f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c82cd586e5ee4225bddfa1c154facd9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7075ce6abab7415987a1e1cf544b6e1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7bce27979ed49bfad1647359f3431a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a7ba64ae6ce44d1bb8946dc2a884294",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2bcaa54362dd42cc92644172109708e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b36c6305bd354746b82e5e81861460f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e285c20f88d4e8e955f15bd63f1d962",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba2fc7bde06f4068ac3dba1c75b416a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37468967579b4e26a9666f5d6342f6f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f185681f7024e4ea464cf3f7851c902",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cea8bb1b37a44e0a5506a6ebd0c5875",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36a6df53e08d4afda3695d704faad66b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6128d482adbb45d2abc2a4a071b1ea2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "641a699131bf4d449bf739552d1bc89c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3526fec278e4b14986b22b3886f9ca1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64b70b2be1cd4c9dabc1e054acb85f70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d1433551ae04258af2908ebe2a3ef35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a20f7f40325a4f53a38af2db2786aff7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b8c19d4914340cf9873b7fec5ec0f08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27644900f0b342b696f85655e81ab91f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0226e1fc01ad48a897d6b0cbf0766ceb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "325a1af01e954281897ab2cbdf953d45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "448e91b0d3984d06abb55d41cbc8abf1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6779bb5f16247bc9f7e01c4494b3aa5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3939ad21cfea4b35af5ef9a2819a462e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc4850f7c5e144e3b5e301daa79f89a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbf3c83edf2a47b6a7749e8dcac74697",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5fd04a45caf4825b22cd6fd6c2ef0b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b335b518b654cc0a0d575033b9cf52e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31cf5a5cf532402dbaa5730cb2ab48af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50b31feee60e4d168329cc406e68d981",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "333b4a3b03224dc1980d90fe8bfac1f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "009eebbeb24f49c4bb3049c1174de6fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13ce8ee57f92485883f4f2c14fc1fd80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91e72423bf5a48c8a05d4e2643512a36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f87c130ee904394867dbbe46939dcfc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f879426b23c040ce8ab6acf1a4ffc419",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac6008a9b1d44cd0b46c0e087582c821",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "354c3f1659b2468ba9f16072daf0ddb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f190253d2fa43f581b50cd045746a1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "562f6ffd3bce447ebd61b5e67b853c6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c98308c96c1e425fbf48578105ebc940",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9693ee9635e54b7f9f78fe4fd7d2d6c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7fe6e8e643e44849bd738e8232fc78a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fee3ebc1c5247c1921ea9994673e28a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5e831babe5046508aba2404599fb6d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f75c65d22694a688312684171a2b9ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f438140a9cf4d95a1db0a3df8416b6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "303415fcc1ac4037bf1dea1b8112958c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7be75ee6c1884d188f450ace7013b4ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3cf36836fde425d971415795f8f00f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72a8a0de192f42f29a739f93357c322c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "848f06c32dd94917b014106477f30674",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ececf27872b84820b5ecbe158c2a3577",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1b8042880eb4674826c16f2b00251bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "100404524ebd4d189e87e30465b59fb2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fabb89af21964d09bbee562721bc397d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88ee2c1c81664b3caf1f4414fa28b4de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a59912c6e55d4a54b936edcf66055f28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4710dcc153a4fd28ea5f44650a51157",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "574ee4f8a49e43bba4ab05263617ec77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c2e4f2076144956af3c932d63c29041",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d41dc922720d4b5da0ea3fb6b9ca47c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88830675f3cf4efcb6bebe380b23c9cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d038af987914a95bf3a7d11ea787ff2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0564158e840f41348810955c251ba1b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2a80bb056084c98979990e42afa0a66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "254eb64fa5bc4b72b8a66c3922f74eae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f41df306a61b4783a02c61771f894b55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "713e2aae675e44b79c45efaf07808d0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26ddb720ca9549c0bc30b65bf5595189",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4f5594e59bd484e937ca00b545ea9fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f82aabfc244e4ac6bec08db6e4d0ae25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "162dbdfcf63942a6a792943ff0a2b4be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9c018231d4342c79e60a0b6a3d75de5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9faae84dba541b2b6ec39240da060df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2dbca77adbc4998b64337b54b31b497",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ef9e3571dc947d0a3732af4aa5475e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4330bfdf21ba4244809fb7908dcae527",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e34418148f7408695370fd3d58e5124",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0020e9cde4b34e12bf6945163f0d0131",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d9457f2866a4ab0b335eeb9af1faf83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ddf1f2c01be487cba1ccd178d55808f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f80eb6ea83d402e829b3b8f6810d979",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2b7c3c711de4134b44275d178daa109",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b638d624778f479cb25f11fab4531a77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "683574e023ec424cb9e2343a2a552017",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c94aff141964d9f8cdf29caebfd970c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3dffe83efcd4e12a5161e384a2c73d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48dae0e0e39047c3a076a5d458a6f500",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69355fc5bd594a2e90389501541e90b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e89919ef3b5d4c5a8ac023a400ff60e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f8b2eb333f04192a53106435dbc03e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52195c694a8f474388b7d0bed7aef21a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2dfcf12a4c364c0287dc392fcb62f08b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fd516c877774390b9acce071dae5d55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca478b02d6dc47dd956cfdc8a464ec09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4dafa2a43cc4873b967132ecfd29d2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0eee044a04ad40ca94cf9cac48d3af88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70bc5e34d011493e8c10720b0f9b986a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e817fcb3ac414348943c25c9732c033b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a55faa4ae784ff5bcca8aeefd1cf4d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e312f0cdcb944ccea7f66c268db8202a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e158bebf268a497ea82348f1775f15b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22a25e2864554d5188c6832d2f5ecd2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51f8e923b8f44204819937e4c5a1babf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b003757f7164009b3dd6d6c9607a3af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20fc01582b87441497149b0d8082af7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b7e5045d3a64f4d9942fef599a86f0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8bc6989e451436284f743ccd5cb0577",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "92fa13e0a4244aff8ff2bef123ce1e28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "505030b99da045d18f8d617839a945f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12ff8a3249f749adaf7161759308c8d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f2982c8895c4296b64cad7c9ac063a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "454d5f498e1f45eabfbbf63d67296ca0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a897a9aadbc44c3eb29e1f4df3389003",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a4e14754edb48a699f4090405d9670f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1cf69d2fd2a4e8f98c70f4aa34a1272",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10fb6bc3ef324a14baa807aefb9598ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e77125e7cb6e4dbb8d62de6e5ef96fa6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a709d7d513fc40ff87313cd3222e11de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3ee3e3146e24b289f11a681884bb26b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eccf84996a0d4036b456dc82cd3afbf9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "276c7fcabad9473b90d3194f6838e057",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28873d6f97ac4aa7b4c42dc93e56fd30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1119222b60774436a4b8faca88a8f50e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97d1b4156d854b7189d7990145ee99ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e87cd10c14c4a80b05fc223653a6111",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be5fea6d66dd457e8c0358d8a23e2b70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "444592b978614019974a7dbce58cd1cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd4a4310bef847d0864357899b06f3cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5c5ea87efa84f4eb8902348732f7c3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d014831559d64d7784897f6e6f746875",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba0f3bc7f61a486e95e496be3323284c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "336a254df948483095705d1d25a37d6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d7711cd15664530b322cf009561bcea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "634d1a333c8a4b70a4024ba3961019bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d260519f7c304b6d8919bbf2d94f845d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24225f8563f94d9a94956377f7da9f23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97bfd032686444caa96d8c333ff468b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdde45a13a814c4398dd5cc04952d988",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0172aa698d6b46918312e9496dbedfc3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b07918865bd3414dba7729b51df41edf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aee55f821ff84a2fb6485f23bd48345d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbace24970374059b53b79718ec0f39d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25f11186e53c4633b5678554d4f7f467",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8d03deb638c4fcda21222fca7e41a08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11b930c3f0bf4aa8bf37c10304473132",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "faeb6cb127364faaaea9dec3cb6351bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "44c50c07e7134135ab82a712cd3528d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb8f715dbfbc49cc965793dc47945a89",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d81aea0237241d28b4e6c7431cefff6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8feebed015874226a861077304ba425a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbccd1ca2b824e7191a5475c59604569",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9998061489ba4ea383851f3ffc6524a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a59949087e84874956c7924b337109d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6365612f34e487182867e787839986b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "287e91d12a1b4d67bff332d28b97df93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03af3da02413486881b396212f6d8aec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c5050d925cb47448796e8fc8f24263f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fedebe124ca4ebaaf30b31142aafdf9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcf8fbab43dd4e1b82c469820b85344f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d8cdd52b2be41ec9215e03e4a07f62e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68e34d10c3b548c5b5863a09115a793f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f68ce29698e4f70b42dcab301734272",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f17493bfee5c4d10a7bc0e5aff29ed70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb46f60615d14e5f9baeca753b83dcee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "832e75f3f15c4bd9b5335c7b7ebe785d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55f209c1841c45f3b2a054d300928f62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "783cf82a434f49b5b5685e67b7bfb7c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be19c7168b644fddabf42c5127ae6c3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d913e24b5167444d8e28b0160f8f339b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c32a26783ca54ceea25c2106c56b546b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "158f7e76db2e4c7893a4c814268cb833",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8196f24ed084a25a4bc85dbe0973a90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8854f56462fa43e9aef74cddcaef4820",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "583a40b98ee14fbeb48dd6a141f9995f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa7a8451984e4315be578e37ed444474",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c226edce2ab54185917f84a2476bd857",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4005f86b85bf49e583f9c33881f22ed0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9293f815d4a4403b80678b718938e56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21ee44dbc11e4004ba1dd1798c13e22e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f49043bef4eb454bb820756f9a48d996",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49c7c19b5eb7420cbd319317013ee231",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2d33d7c957c489a96d5f41091eefbba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b48c15367564788b78dcf8f0931539b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4bccc2bd8d545c0a22982766551b8f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "068f08ab21d94c7ba4de5f7f78a5d758",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4e7f8f818a84beca04ed3ed1222d8d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da560153824e4b5d939919f00ff5551e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "246881cacd9f4c888ac8a40d3c982d30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bd84f9a2bb141ed9a685a24571a6c70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42a4ec8cd52c443a8d2c0b2501451a62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ba0f94dd19d45b3a8886368d676547f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "760d2b710cf147efb8952526e15a58c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ef18ad29e3c475db81c76795bdae862",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6d9a30bccf24857bb8f4397ddf6685d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e20d45097f8943d88505d2d2e722d6e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af07e078d44b4e859a2a3b57c05c573b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad79b507971c49b1a49737463a8de432",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4def055fa4ac4190b858de6ef5ac79eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3a4ab5d703f45049067ef1f9d1d028b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfed90b67e6945b4be8996a42e53000b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62b66cfa3afb420581c1c4c7c5342571",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c537bd636492462ab7f59a20e312916b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77bc89ae29f148b094aec0d5406a2cf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a670132667c4d64a4726df5d76c3c96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cdbceec560e8497eac12c8a3c2e7b725",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f2dec11ed9846d7b1c2bf5ab6773aac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19a8c17b72fd4d52a876f78a912ffd45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "023292b9d8fb4e5cb2fafb3fa40ac2de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f843086e0e0d4e4ab51bdd6f2b236a72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d7cfdefa5db4e12b6b30dd44cf93c56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77bde91d3d304caf9e36f6a7e476481d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d001971199f2430485a769df39792c1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b222d6d7c6f74191b18225c0f4968f9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c8166ba5bcf44ccb1f8d2183d682918",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77880478a2bb4ec1ad988a16a204195e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1700ecc58bac489685b843a26053fef0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66c431959fb940ae88d10a98fe1e4493",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10f250a62df242d7ba11274fb199eb8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca5dd1b557e4448f980843ba0b2ac35a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17e9d3bbcb004b719f963d39b020cfba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9823e69aea4f4c5485a09eb35ae3aa0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b00a6face254ec0917b59a227c8fc56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8135b642c7b441485544925d1d797c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86b6e68ab6434fdfa06fa50f9011159c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e82614b03ea42b9932dd902b98826bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c95f99ad45334518aa71bba8f4ef61f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3713388e866f4536bc1eb5abcc10a4ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5e8d8098c1a423d89e0e133645ba967",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45fc02df33fa459d929355d3faf47450",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e46cae11f2d425fb1c22f4c44a8f2ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f2424ce56d042b5a8473787ac2cbeb2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "965930b6ee4e44db914ecfbbb0d8f70c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23abb9f297964eac89d460fd2fe91118",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e52ebbf5e70644f08b7518c0a2666b14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb9820966ea34a94ba20bf3836db8e8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "880ba55e867441728c6db039866c45ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42ba643c5f4f4c46b249d4b0355c3999",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03bc24009e4a4caaa7f7e9b0b2806148",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "609049fb3e444233ba99f96f447b8abf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8908a6adbff5477dbc8d60f22119d3ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6b23927bfbf4363b62bbeaa032c9963",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3b89a3b75dd48cfb0d0ced0f066c20a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc172a5b2e9b462ea721f6f47034bace",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35a08cb088544bb59bce39b79e567808",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d040047b6f6448fb6e93b56dca6b522",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9a7e8c092474399867b7af7b10b592b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fddd752a18fb474ba0515ab50bc078d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b38e720ceccc44f2bae1aed5c8bb39b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71161188907b48da9276bf001314b84f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0abee0ac524e465781c91b9f1b6befaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1cdfc03908541eda2b89b790288ef9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a4d9ee6e3f54e66ab9ee2da68835eba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48bdbde9879d4a7ab5d5b89e03880012",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c70e8417df7b419fa82e6f8f34b668dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93118d0de5f645fb9ce49ebe336c885d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "deac1bbfa20b4a539013c7b838581cce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "faecb94bd1304349b7cfa8cd5d177a6c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25a1d64eab0347eeba315c0a8bc46923",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "580e9652f04a4592b0bc1a5e29b99a39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "832f44b31ee04f4c98620191756c830c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c803c60c83e04c528fac731e943a6553",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "960e09593edf484d90e0d53aae5c260a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c24fdfb4d56641fcbc0f89d08f672cd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49b8a57138d94fbcbc6f894651f966ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d3c8ee9754a43538a080e2a21e46e81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "973b8f1f665048cea0bd166644e6f3d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a308ebff6844c1bb023f784237a2968",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "282b6dcb495040678246ac0153064007",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f082a5fec5bf4151bd1f85228873f19a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c2f5bccbe2f44b8a7fdbfb007e133a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b7b90f66c06428b8fe9fe1def0299e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff23361aa04a486fbe30dd4caabf75f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c62ba29127e14a3a9942a1d3b20a4291",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca8fbf2023a24bf791e07317bebf8aa7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e416d4e9d504e309a08846e02d5f159",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55062019a66643de8aa7a7b65b674fbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d08554e8ad76498b837380911312dfb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7928794b306f49e395a283cfa3412b2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e752204fe613450495de2a00c017f468",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54ce1d0ca5f449128ab0ef43f012e456",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed3498b376a04e32903a55c7b27e99ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "620ea1db091f40db97fdf6efae575671",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "259fb0aa994a4621a3fef6b15611f537",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d0075fdd5bd47c9942d6ef6d4c60a1a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b37d21c6b5974fed8f952cdca646eaaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0769085cd8c43eb814a186edc093f86",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5782429ec21412189ea1154922f943d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3cd04407f2a94b18b35bece82e90139c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "653ab700b1a84a19bdda5f537ffc90fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f92ddc770b434a618d2599fea127b6e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae61500d46184e6da746eec46da3797d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8396331e8dc48b7b12eea30f8616a50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96832de13ce945e6bfe84472ae3003b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c00a9ad2e84a470588edce39fcbb0a71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "deb1f45e82aa47a0a4c00cd2d47ff337",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dba29f46560943e2b65df1bf811b56d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e891f74f17a47d5aa0210d1fc46258c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d02866f675484461a589e361c4415696",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f490899a5c44fd399b4e4319e342f67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19377cb27f724f33b9754c30e8c33258",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52ce731cbfce4bbd8ae00194082c1696",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0aee299ec694f62a1545af99b4e1335",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47104c2210f548f78c233d6d5de6deb8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0d759dca13b431caa69ca492f856147",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c13f9165952249d3b5f1f3e45dc0aec7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a6504bbe4304640b950c8e97493bcba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d15204b7d419441186943f66f4c38260",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b77dfd466c32489d9126d9f38b622011",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20abd98ab8fc467bbd7edf13ce630c63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "729b207df75a415c98dd8e36a692c912",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26f244638c374f53a9a21ebb10832609",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f87b9675761f41b898fdc84edf95e6e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63f88967cc75417886b3521980d2b1f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "678711d674b94fe4841d22437ce2ae1a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd194310a6b5420f8d60d6efbee33f2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2439247a7e7a4044a7bf797ba6b664b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d4ca8eebcb543868053c73c82b29a6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b372b8daa761464eabc7b1180a14e7aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a065b1df8ff040b094e89020e86980bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12b3069146484d75ba041a6843faba0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f90267d7f81840b3a6f90e33849e0b36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f29cdd9387b4b1381b39993248fdf38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f4a70b8e5054d0a9c428369f1e41c81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb55a8ebd91d45f5a01fcb08d0038e95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b17cc73a79a4f34be3f86750b21c38c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e154a1e9287b47609a0df93587e18ee1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0272eb8e6cbb478ab81fb793c26fa27c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "760dc2b911ac43a6a508df4f6c14e55a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99e485ef00f546dfb513dd53d7f17b41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d439530b32ec477a916fe207671d4ca3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66b88332519b4f019fafc4e889f39d71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5678fb18fe6a4238b1d24a849bd127dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "581ce863e92b4d7e854682220ce4dfd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "935f09f67fb649c0871fab60703f0d2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87d10798d54142008c49693133ef253e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17ea5532ae924e31919d3423e385cad3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ff821ea390e4b61b912e51de63ac6ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80a82c3445504ec7887e7f13045a5e76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d930794ae7e0465db928adc6c33bbf60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "baf34c5374af44a1971ddd3b8cf75d2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df97e3cc5d474747b925243cd7a66709",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "395affdfd28e47db83317d1161491694",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c556c08c7f1c4985befb2c1bf30fe49f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "277a4ae8e4a54566baf8fc46f899bc9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3b0e9869f1f4d2e86fd0e15ea794dc3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e0be710401e446788e6b8f5de2baf39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5885cea6e33a4d478026d33b1821fad2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edcf26df6a444ea9bbae038b94ac020e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47bc3bf82eb3442abd3bc3c46a0ce268",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2574d2b00cb94803a6d7fd249aa2d120",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a18679e19c4f4b6a99fdbed5842fb23d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62c15e5b25d8488bacabb106859bfd14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3cfa73c7490941898551c17d9410158b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1794a1660c394348a1e88a7ba3f0718f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae8aeb872d4a4590b152ee9579a27de0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3dece8713e5945bc9a5fa2c16fb63f3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6d9eb4ce2124c789a296534fb4ead00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a14591fcb2584266ac50c648d1c41dd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0736d1c3aeb44b2b7ae35845242772d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f43c896472734cafa0f3fa948cb6c762",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2d39a020b5b4dd0ab338157fb30de24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cad5254ebdc4fb3b7ce1d5248816af3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0a6f854f1b94b2eafd1b2ea078ea767",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4179214bf9bd4842ad3068228677275f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d57b8480b4b64874958aa854f2dae4cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06f8127f63d3470cbf67164f44dc9311",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "863eb87bd01244c3b2d536a01ba48d67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd8c418a1a9844d8829f501d8254fbd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "995f884806ab4d969ee6891778494155",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dae65dd3c5b941dab529d358f1ca1686",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "268d7d8708fe4161bad8a2f3b113eea9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c5bc6b6fde54909bca1620b26571f42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb7e14706c27432c9d21f83d8142b188",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "168f175d9a1641a9b38ddc5591bbca15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20d8442888a34bae84e499b2a089a5f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76886950daee430ba377ab345397a086",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99ef0e73627f4b5998d1cf6e6b65c4df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "307473dfd2da4c0ea6076a4031bb6b19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71d312ac546a4db281d257d21430d1e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c800f1f0542448bb7d2d8b63f5f3471",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c63d6e362984c12b11854c8bb996ec2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fc5eec944114f0e94014b453cf5ffe6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88c5aa46315a4519b73d2c47cedca9a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1942db08f34648a9bcfcef18db0f7827",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12097d36381544569cdc9fad38adf11a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99f1d8ef041a445581b92dec8cce3d2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6d572ff18fe4e19a5a642eb8efa82ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d9db370ca2147e3bab13645a4f44372",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ee98175b20f4ff081508067b4e5fabe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43380ccb403547cdb8ec06e785b48045",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2879fdc19865474bbcb696a96e844f1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9bf836062204bf281b048911aa376c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "747fbc1655434196bdd8c745e778bb5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6e80a821c2b4a9099204cb8a05b28df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "877f4feeeff647928cc53ec90fb57df8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "224e75e30a7c4746b7a53f427b12b2af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2bfc817d76ca44ebb1a46a5663a7dddc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de957a5352924ab29797829af48c1f54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "073b2af4c3ac40f8a288d32ed197b140",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce1337440dff48749fc1932e9fd30a79",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d819ebca1ac04f4c81797cf4dee51c5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "660107384eeb4d35bdca6118ba3ee5ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e28cb70a8f14cc48f0e9b24101f5023",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db0d4a282e1846b98bca8b51780cf9ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa406f0b187a42d79577205c2aa3d7c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07f515121bf744fab606882e82551cf3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60bc7db0ef2d4be09663b276dfc88def",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cf1315dfc56445eaab4dafb0fccbe1a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71d687b1953644b29ca4f2f0efe55b59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5f4e80449884200921b68c5b6e74fb7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45e8a3a4db334036b35c004d7bfaa17d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f7d5280c6b94780aa836dbc332986d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f3f37c313d24db1a846fd665777cbb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc0292a2f8694637b51e8138ed0f133f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "970d86699f454951a77e365794572fac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfe6c8630e1646dba7160cc5cf9b2cc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2e570d8ff294e008032d183eb2575ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "180549b320ed4c29802eab3cc1d3b9c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c760e62e536449e951e872a532f29ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1468fb234adf47ce823d7a38d07c3939",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e037bac3b364a2ba5e764d5d80c8848",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "655b0d7debe84d149acc90504988a7ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2624a47a2269414c857f2de7e63bcf4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abd1fadd9ac944d3a84110fce6280eb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54c3d7c16f96451e8e2560ef6c2dedff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6277a988adbf4784bd7899f1b724fc5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f624d828d34c4aba9d78e7675a61bbe4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0773ea222c444dbb5e46f2403ed22a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8e5d372f20343d7998e014401158da0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ecce6dd5fa94482e85ca91d913d761f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f78c8eca6f94225b9870ce7953d0b62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f2585fb3fb041c68f820b63105707a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b06946e0f28d4846a87a4fc645d9aa02",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f017fc7ec2fd42ef9e72e1cf91434e8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c548486c16644318d2988ff7e56abfb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a9f3f611b8a416c854dec3e143a9d28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75b43fd5bd8947dbb8ef88e0852fd2c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f91b483317c4a028dc6d339c698afc0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c88ddff7eb8488fa8a13862f2c9e6f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf10e602774c450fab3ff5e06cd8cb48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebc26da6719a439b8b8485bb5883a304",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9ca914cbffe40f9a86512688c45ee59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1748eacf21b34e3d87d6f531a9c28ed7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "866c71c1ebf740398512783ae63a0e65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b521657b9a249f5858eda8a3385714f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "959620f9173f4777888cb2c1456b3aef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "946b365168ea41ecab11e4619b39f689",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4eff9d77ab5746fb8ac8cdfa5157c82d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "425ac4d210194a2fa572c0a3d7a2d643",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29647f004ad9414a98c8c0b3d49e0fa3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1561052d961d4df8b5212cf048de58c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3f7c577889745fba50eebb3c2e9b769",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4910ce56e6244e7a7a0930c8d5dfa96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3397ebd3ea8d46be8fe772970aea701e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14f973e5966d450d8486e13225c7e29f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6c133957e5048838506549465e9906f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77b038e207c3420c842ed1f939583051",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "101beb2bc5904aa3b1433a04adf4fb6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "437fe723a2f14a6c91d251c8626b9fe5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b42c1b30631445709706cc6e4cd0da1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3cbbedf6426543a5b331e8929591870c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6711df9b52a74b2eb2d20f4431093908",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45691df8fbc4429b98d1ae3b256a34b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b7433ea1fb34724b25d7c7e32591872",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8c0fa71f60f42d1b2cc282050c3d016",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70ec6978b80e4e9c85dc61298adea3e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99767ef4476943a78049f999b4fcc071",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c8f61cd37da44c3abe64069f7b74b72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ddc439b522ca46538c7c3e8e52de3873",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0be3400b46e41e09794ac2313e3b23a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21949e4cd0094181872571a7c7eaa1eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21f9d5151fc542d59ab5c26a78e03480",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "707f3bdf20cf44f8b691ee6258f6b0aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e22e1a49708d4872be07765b4f7413ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebc23e71e317463fb03c7ccce766f6b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6cd66ad6b8b4ccda949e05a6ef7a02b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ff072c2c04a46849615a04d396e8212",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3ef168020ad40169002434ee85dc655",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79217899c42946929713ea654395b0c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b246decabc064089b19775efb728e444",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16473a733cd54b25a607a7929cc8bb10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82ded2f4c49e46fea9b67f5ab3c00697",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19bdf698b6594164a5b8a077902f0bad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a9aac1c31014232bd9dd0ffcadd1784",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "897cd1f49d474e7fa88511123e046101",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32112bca93bd49df97d20bf207ef81db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "848ed2d4827f49769a68846e8600c3dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90fa07782fd54d6b992641157e4851de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56478ca8d59646f4abbf6d67c7a285fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28dd276458504f4ea17bd7f250fb38b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00527a2caa114e9daf3ecdc993b856a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a61bb7d626c41bcb60c4a6699fae536",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a9ea55410cf455795e97ee44b48f027",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a2c263faf2740429ec0fe28fa9ef52c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2bb77ffbb4c4933954b2d8f55402510",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35ae50577f0c44fe9ad2ceee6ae6ba1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be84f549248c4aa8a6ddc48c7ebd506c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b9556afed3f426a9fc00a0902c94873",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9f8da9ab86c481b8cd02ae240a85bd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ade3e883561d4c50bb5bf3c05c4bdfec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c31a845b19e4a76ba4ec13ad0fb1219",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e23a0e188d0d424fa3cdb7a954291b23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b74cc84ba714225bf86476ca8490f96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d95fac9f2da640c2aadae6c4b2cf3835",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb2fde0a602849a591d83de81a14b10b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2089020209c40388f4b2a8f47fea392",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bd7c0248ef4401397ce9ce8f34fe29a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62dabbaa99ea4579b35303adbcd31269",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b0bd643cbfe48448b15751ebc26cdb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f112576da93498986f840c558f07fb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0db4119b4af4d04a75e6b3e1574aacf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da91ef96a10f4894b4e9940f1aa20547",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58a08c3851334fff929563ec592b64bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa96832b046e4245b6b246703968eb0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30853c45eb2d480283c32d6fe53c7614",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3ef31d3661e4c6784e0b94af7871ebc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3417669bca2455a9b14c70d7c774cdb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdac6c4c6ede4eabb0ba45312417cee8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7226dce508d5455ab7aa7f3f87d01e38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac6a8b904ec24fcd9e45e1a16f99eb51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e762fdc98dd4db7804c1a46be2b8046",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20758628a092442b89f49fede9676262",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3454cca1d6be4f158391dd1f1b421674",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b73f6409321475194b405a0fe9349ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "964dd4afaddc42339b4a6b95abedc059",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9123bad9cef0403ba62a9ff3c6ef3692",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "208d90e11f6c40c4a31accad3947e177",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "051e7b645c404ceb9b6e1d5bf58c0185",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "359d7d8a7735434ab1a93bec8d7def76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffdf50ae98894284a11cb30630e669dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acbca78590de47fd9d26095bc2ccc962",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d137409f4bb04dad92ee07cb6999fe73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8baefde776b3492b998275f94e3e7b31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61e2db344a69424eb565f9e8675fd77b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acd1aa66506040faa6314ef2535cd99d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79076d70f6744d5f9bacfd0f7aa74f75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e93bbaa68c704b10bc3c68eab4009d59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fef636d5096d4eefadfeed1ad0d72fd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1521a7abc6004e74a749b4b4b0481349",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb2b80d735f346668c59b7e7e5c5ca95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45ca39050f7d4990bb0faa9ea41364a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc126a0646e5471783153dbc099d3255",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf9efefd14464bae8320f819bd803a5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd2c76769efb4d87947e99f719dc54a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a24374acb38f497f8be9f011f58b1c97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e6a353b6c4f415e99c9dbecd0bc09c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb18d0f6dc904cd7acdce31b28fd64d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72205550461446fc8545b93d3b73e714",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "605df59b17114aeebd73b3362cdfcdb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac35e362c8c34bf59ea9c1c044db2b2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ef8f938468f49e2a6e74a591a82ee87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "005f9fe6de6143e18aaf580ff5a4bc5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03f57aabdc1a4db3810e8accd462e632",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e1afcc1bd2a440c9f99ed96218c5caf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6c0e8b38aed4037a0a5cef2162d40b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8589982738534c65b64f02f88c7a1063",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1d980b4ac174383a5666b2a555cf3d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd0a0da02c46421f9a9aa5695f6d8b55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11fa280630c643418efba4bea09a190f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bf1465fcdf44b9ba1d8e44794071a7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b098aba9cbe4da2a3d01b7f4c88c2c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "018a1c4fcc7f490db49244246b593eb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9adb23b5af744d02946e6f7162be4a7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c5acdbb0bba4056acafdf0f9d8bad1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be62b3af9e4f4600894f15b28e80be4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e9a4c2b304f4280a19f589725ae7409",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2e70e4ee6ff406cbc188d43ff18457d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dce25f1e2cdc4eb2be8802e95a254a0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0e6d19f754347138a68f4650f31b7f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "717be0c7140647d389432088a57643a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef4b5e6a83684265bc5865ad401e38e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fedf957f74664a7cb9bf8d518c1bd115",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ba793b54f8c42d99f521c0e278d2299",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2efec584a86845d3ae6ae44917eb03c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "297074932af84f1f81834b612bab4a78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49f51bc808394d5487c6bfba1625caf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5cd6ef9a07249cca0f0cbb2d90f2126",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87a4a06bde4d403aa41d7f4556161126",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49a1db91980e49b39e669c5b3a41f896",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "707267bbdc6741f7b7f9da097a8c6e43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f900d332c35e49f4a05bcd65afd971cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9aec5eeeb62407a9dfc44eed657a5e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "880d7e3e4aeb4dbebcc7202fa9501a07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a022ed8714d64e99874f436c43942d31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19e4e9a0491e42219b8b3f68053d62ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "247719194ac24fb697b5af029d4b016c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a275b32d40a492ca7874270204cea86",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d53a6b07b8774366a80f1b444c5ba6de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d171d02508944ea1b7e2d34f1b090691",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f330d1829ab94315ba69df047ce71899",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "106e9461b23b43a2af1dd984a10df5ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dac2be17d85445e8a87654e4fdd3d9f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1298a0ee09d44dc095e5dd94516df86f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91142746d62e4871b653bbe07d67d74c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f168138d687d4dd68f2d956bc8c79bcf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d170a9a81f54a12995faf7e06653a97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a60a907ac6c7419cb74daca957a8fecb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60db54b4b0cf4b3da36fb90d0c7eb4ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6549ab1c1b38469fbf157f61d9a11db9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2a543a6cfe54f218a094495b3f52217",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e6e8966c9ce145f1933fa6beb4d8f609",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8d24fcc021645f9810106a0c3293027",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "468cbe4dff2e4c2e86e70814ff636def",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3facbde194c41bbb9e1310db4c48a8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed5af672de6647deaab2cb027fdbeb83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b47ff80738184f1da18c55f3ab2658cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a725a3a1b1643348aafdb0612ea7a7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eecc8f88abad48958cec07fa69de6fe0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2fad991875647209e46fd69a255ab71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54759a8189374ee1890f66026fb1eaa2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0ae0b3a7e30455085e23b8d7f16a517",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77b9f15e499e412c97f879c683ca786e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2849f39522ab4d4b91391a5f0fb23a40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b5d460e404544e58f8070d87186be9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "698f870d53ae45b794dcdfc07b05ef71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f62df00e32444aeb9e2f9cd6ba8cf739",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "157b6296cae64988bad46527a6a3f894",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bcb6be2e87d249359c0f664c454ce5d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7369e998eb54a1b98c5857dbeac7bbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b2f06cf0c05476caf1b0459242ec34c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a054c07822a74d528ffb77a39ef29499",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7598f227f0243dfb984ad9afd59f315",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "288ffde4314747acbca2f2f6513d2310",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "658fc9abd3774bd7b748982b6d5453d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fab021f3e2b341c1ad02cc7bbd287104",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "faeb0d9937f540a28144bb2fe8abf401",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5398e50e3be9484fa85c032315bf1667",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "857afc1525d04a2bbce91b6ace8714ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af8148f92f6c43319aa327deba43d575",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c28de9bb655476fb2cc3a5824f7197e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbca266de8b644fe9e7ed330c71ef5ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4a11d99978e40eab20e1f6bf74db057",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21d86f0dcbe04eac8a3b0348dad2d0f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8269f3da06444ad781549c44da1d5799",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f13a78d3addf4c34a5bd7c063b1ab1b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8129d3d65f32408ab30503e9735f1f24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2247ecdc2fca457e83ee14f49144d4c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f527d475c7f943bd9a08b44f3665e680",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "129a7a86929341c7a76b22aa88e5ce83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a09fe2c1129410fbbf42f3eb10ce702",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cd2cfa61bb248d79d57e2e01d4172ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "125fcfc3967143b7a9aa7534986296f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25131b4925fe47a8be1079919095cee3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87609674392e450a9424728d630bc2e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac71c7165f2e49c4ba0d88744a5619fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64edd48e35544a12a892a7839ff44e3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "766edc336ecd4264ae8b32ec4b50eef9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdb5329009de44739ede1ec66fe39ff0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "457216b183e948ffb81efb435270fabf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67fac703aef44f9aa2d4229c8c413e1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a031a8a88a6407a974b3d00a77a23dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d201acb43010481aa148bc3097a5272c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db321186f7d34a198777968f0321f417",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b7dbc296ceb41bcb4c7aeede398c2ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56c729117f594ad3aca0cf618195d42a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "564eb0dbe660423097ead250ece7eea1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcdc723919344f52a520211e3098be1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10c104f8a3684e0299f9a4b39b84816e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99a702f5a6d143ab9dc65a6d071b3089",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf350225889145e894d64df5525e4ee7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa8daf56c6a547a4bfc5f6ac5d38a348",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a07652a593bc46e9864e31a3c1411c26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b3475b1ca014f9ab18ed9dd1fa7d251",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5fd24cc597743d6b9f118f3ea46d691",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9d38873ab5b4eea9879bb0b15211167",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2151369c7d2c468fb5e49514a94c487a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0f80b4f895647e1a1fc3b406fad91bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a07dbc08ef1f4a2c82a29a4e0909bc61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac2aa6ad6c1e4dbab91fe480bd92acac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c40e241bab90417897f8a282c94a64a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56524548cb394742b1b55baae4fc0390",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1184a44414e43a6af0ecabbfff8a1a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34a3f1ebaa8a4a8ab755d578f66c742b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "265c1fc014bd4150b2a6a7bd04352f67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "768e4f3f3d4041078ff8bc7c19f3c932",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c89df64c48d54e758da7c529fc777fcd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "924977e10a154eb188215926a854659a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9f76bdda20d4b2da621e1a34d4076fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f925ea13f1c5429496d4060246f68eff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bcd45a0009c4686a17d08c1f2cb5fc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d868a3a3e13a4fd0a25b46c54fe86413",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "811ef4879d5f4fc5b20be94d323cf4c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b805f3fbed24557816fa8cf9327d21e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "08286356c7e2467d9a1d3d3250edcd20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8be7fe87bb8c4692b0cfc16679172afb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c19063d97b204c5fb5f2af4468360674",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbc967d13dd84a378a548133dad7ddab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0d9d5a9a85347a9a6f648b773f7ac41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34db573121424c5fbe088415df300364",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29b4e4fe06054050a92a0852bbaad435",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9321ae9ba2f447a691bbc502510fe934",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "593057f253344786aa995f7ef9ce6244",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "998bad4fae494184add4898df9152f9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbecf342318a47afb55ebc755e5a15c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16059dc62fd3454dbf7ce3298de89703",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd1617b5434b4b648bbb949f4b05b69b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39b04e750ca74496a5b5e67a727b98de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8706cf4d73aa4647886dc3bc5b437c03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "08cc4d0be2c647b7a95e2a100643c95d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67f98cd7c0cb495b99cdf4badf4d117e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b23d659d9b5f488a9bb6df345732ec2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ac29b0fd71543a4a53399e666475e8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c7bf852e72b4040b98cccf97b5db9f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78a74e5dc8764a839029e6b60f6aa570",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "905f53d7598d4e3eba1b185270949a27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ac496475f1c4796b421f924c70774b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99f4d54e2f774ce7ac52fa8076e69714",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "baf0ab84d7414b5e852fcb063a3dba45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "979bc845d9c245509099c733d4038c90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5562003e4ee444338c9052b2e15983f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4486ac3505094bb394aabdaed96b4a1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28c616f7f5394762add4fcef6207d6a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa3cdbeb5459481f8ae6f6c250923ec8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ac51a5744d34784888c3f82deea22c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9265d35d1e7b4e71a5dece3a8298e6d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5dc72a49a22644c6a8d6cdebb0845518",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84aea3ccde3349d48c59a292394ebe73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da37deecd78d47d5a817b517648e994c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c52dc53ef78845ba91dddeabda7deece",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e91d4f044d544cf9aae06a5f872bee2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aaba48b6749d4a7a88edcafa88025eef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0f3410beb3849aeb3894ddab0c25b81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bc3d40f8be34b418a3a231133520c18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8146a66147014627b0ce44d5a9be904e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f57fc816c704e3fa7b5f02e5a08e26b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6e48c45fe7e4b769951d222b695dc77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e76ab773329b47d48d6a263d793ecfc0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c115ca25be804148b4023139fac5fab3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3af0a237677e4a85ae07cbe53b52e776",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e7e9c44d10b4937aba0c241334c9837",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87e3e7d123e84243843054b21e7cb545",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f8ca70fb4c544b0898542525f8fd78a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a09be98459b4411b0c443a375a7a41a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9f9520b5d7947d99816870287ccf8d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8eab121f7f5045e08f01d407b83a739e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05febf8eea674724abbafd4c35fa9bcf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e92e299391c4496882e461ca504100c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "768fba36464a4b21bc3e714bdb4d6bcd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8dfefe5e390a427fa18b7dfc32d278ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c96c122efb154d1f93fb8d3c1b6b4dc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7bffca95c604fb1a268a7684543923f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "941112f52d2645cd953b7fc3f2555b1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54a7a434599846db9298219a641a0ba2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c08945e630f4905813dcd70102f2248",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ecc0cac5d1d4834bd4a2c8719a8d323",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c7ceb16218344e0a92c29a181a223ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4006bdaaf7694f75bbb2a2176f1a8652",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c84ce176bbb404884bce68ee2afbc45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77c5bc2915b24db888909ac8fb0a3dbb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d290e900fbb8433b98cbb5e63cb711c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce9394ec80094853b57a3c241840435f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "179fb8ff7c7e4bb7b3dec563aa1e6434",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a47f12197c474fa9aa712c558fb785c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aed749098c3c484bb363c260d5043264",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef0bf1ca36704cd7b72bc5238952efd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34fbc360ddf947949d514c91dd67c077",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5240e8f1fd044ed08dac1a059e18cacd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d91bb2fa88f947df94c752a92b04697a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc14fb2bae1a4e639e471bd8b51b946f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "941129c74c574ab4bcf095cc96e08867",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0998d947c0a4b1ea3aa24ca06a83484",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5854869ec5794cc283a2b4012f0dcd1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1a7480a16c644888a445699793be653",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b52a5e1235bf467b95fec4b8cac15a97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7841d8aa39824ca08daa33649d71a4f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6468fac755484b1dbc91b390d8599aeb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a7247f94a1b411c83f8cf0da19d79fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54f63755811c4c12bab7dbae14b676e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b82dfa36a2ca40a890a5a50b0cfa79af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24b445a42a87433ab7a8f6a86dd3cbd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c4927064f8b4491b0165dba0c3c3e51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "820efe169c90469c82e9e4087f81263b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0164efcc422b4cd99ce55a9d8ac8ceb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f75fccebfc884a1c91ebf979cc5ff295",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1baf8c650f564148981904b1928f2ef2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41a1746d4df849f99795f8ac3b45a2f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "774d9751271143a88bc82ddf8cb200da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48f9554056814a72bbd785e6444066c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "218025d5a2324e4bb1a55dde64fc27de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39a5926f37c04134b4d646f6c97b303f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1d27a13d2264a3bb52b72396c3ed745",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af9f69cfd57e4a0cb7ff533e922ad838",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04cd9df26c8047f38d4309242a61c044",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d46efa5ad87644fab50ad12d7e0519b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df3bc3852bb94775a344b99a7f010904",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a2bde56aeab4c239b44e1f03dec7fea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "421aa4daf9654f9cae7ab1fb4e7e0294",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f3901940c06402ca2ac9e92a7401c62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "702ad2e83f8a4819a840734be024afbe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7a7b297e25645dfa6baf42123fefa9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e1b85efa40f4f5691ba9c492f314363",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c57fd39f91d4ea0aa1e0bcbb9df5dd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a94aff3bd724cfba44a8c3c9bc8aaa2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5133bf8d2d0241dcaf6c45b55afcaaab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb106f013ca54f5db8402ef88ddd6c8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f70f68e053064f0fa9fe7bd3d408277f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba07a4ca65884ee284ad9981706d84c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "146b1f4c81304900bfcceebd551cd652",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "859a25ec19334c11b548d90cd474a6dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e336914bba834c0594c497f9c24cfd2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "44f724254fb141c5b703e5d761ab2ee0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1330c7229bcb4e78b7f0efcff44a094b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "415b15c4d2054fccb737446460cf3b56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67fb0d773dc942feb8d37099cb8e2de9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6b749a2900f4187aba958e8352c61ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c44a084441e40899e1c173ff8755281",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87a57163a7214a15a8b459c49577fe29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64123f61b7de4041a5f1201cdbd5d214",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "638f20bfe1f74bcfb65eddb24dcce8c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec2442113ea94272ab73dfccb282a00c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da32d8db74a0483db95496bf6a49e32a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1c5810212ce40f1bcc0c9438872e885",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf5f0d8161bb40b6955e76e718e9fd9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70a611552a0e41d1919200659fb89f1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4076032ba9114c0d9c164eb4f87fa881",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75a8468866374f64929bb71bf902833f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68222cfa2121478ea559fa1e6aadddef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1528465f42ed42eba7c22559f79a7bba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f1d64f1344142a0af2a04d1180be57a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06d963d78a794555bf6570641e7bc35e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5fc2cb34bd00450599f773f5f20066ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6377237bf2a642fcbfb3ad0bc06a7509",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e941741bccb47199ebc065991efa31a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "447d3c03249a48ea8d448c6c52c14b49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2b3fb432c254670b5718398e011b03a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c09e11ba45543cc89e56d4cb275f60a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa45be8b14534cceb628a499a098beef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4308baa5547b43679d3ef58733507ead",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8516db3e91246da98e77ffe61acf195",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67acfceb5ef74df589892a38c031d484",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bddf6df73ee647ea8df4cdc18124ce7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb90db57154d43dbae99532d3203601e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b99ec7eb0283485b9d607b23674526a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdeb0808deb840399874a0e46b578a99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5bfe2ed5fea41ccae832f56928c4dc3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b07e47ee09440daaa53f328c85821bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21b0f52c2f14479d899cb08cb011373b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88bfabef226f4b1a972cc373aa851b1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85ecf47f1b8c4c948aa2b2699b35e1a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eae750e3bb3f44ca8a32ec01641ef470",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "375c3afb86084140b5807001e2f20b3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86850e00301845d18bc3f587010dccff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e13714e97ff4d46a340071e3d9edc21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8388c315ef004af9b5a126d02d032aee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "403b4e3dbcb8453690be5abde9134699",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4217a065758c47efbdf24f5ac8551cdf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c08f9a23f07242ac94dff39e4cf48d00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d09f8bed2414eb095d14975928ad964",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1a6419810614030b8f1af008a9ca3d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89c8c67b80474b9e9ff739fd04620d74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "951f13c5f09f49f6a82e03035819fd58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfdb974c3bc84531a4341ae965ba5433",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32d8d478491a4d6a815e4914e5716778",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b09cd747779f44358b5eb653ec09ec37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0751e8b50e0c44ce8a437f45fee7e7ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2a1696488b34ecaba6a6d4eb89aa889",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79040dca5fa34eefb5c1f75cbdb127ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "918211059af443a78dae50bd8832fe52",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cab422200b04b30bc036edb6285891d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b79679e9da6498fbd63629dfe91ce00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "033bf3f5eb824f3e86443cb27835136e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9c48f34db1747c6875effec049331d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50d498a695fc467494551f7d05d576d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26b332d25fd84c9fa0d5588b957f4d3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "685d0f0e940c4c55b53eb0c5059124df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1456d6629e84b9fbcc83e12b9d94075",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c08eda7b1b36471180ac38ad64951eca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9444c8adb60044b8a8357152748ab2bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88670aa2a7244700b61a22f29aab28b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99a9c3b920bd4913984e21b1e42969f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4c04e50dffa4f01b3c487ab412d02f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3134308d11734f6b85d8f56291cabe54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abe2ceb79f274e7ab42462af2b85eb56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e7e88d8633349deb7e5b9e42045d109",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10cb063d121f43b48f2c417b52be2dc0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "380e19d2aa984becb62faefe7902071a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01934f47d31a43caad6c740e891a84da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aea8c6be7ba142759da6d6c6c545a12b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c9ac0e373e84ec5847a64d055fef7ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a17fda4b0f6445d694d09bc41fa53c43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b9c5d2e2c0d494d8c7b2097d5f15a4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88eaa5eb1ace4246af43091c73621f5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74b1bd1fb6d644aea28cf88a5ac3bbbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce1d11258d344b5da7418f8f60ed2cbe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ef33961d9d14e31acac4ca22a5bc18d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "874f582373e0434eb7e4ebffd5dda8a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "585777f64fbe44de8551acb476747fbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5931c15054b4bd2a052bf86f30e533f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80cbe67dc1c94c77b2f1473ccfedf5d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e06090f158c649b595d5b321a0918c9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12723dd8782543e1bb030fd6cb3227de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16ca25d0b55241bd897b44135aaee826",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abc64aaa6462478e81cea7ac06ee127a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e1a52d93ccf46f7a9b6cfd6a058b681",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4addef7e9f64770b5157458ebd0d03e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dde405f13ac0473681a18631f633c053",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e07aab4dd224378895f67cfd0922e5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29a93a3abba2427eaec94d7e6c1a41ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "602a7ab83b024f0989b43ffcd19af752",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bf845def076412b9e5a936bcb429408",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3906303abed415aa9bbca600f66df30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1254bb6e7ff24962acfc77340d7f2764",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5f42c1a2f5143328a0a803166ce97f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aeb28078099b4ee0ac2a4d44d79ca6b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad8f06574d814966a1dec1d5389761a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc3f4873ec4747079f19ea86bbf9118b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0ce2abdde024bef8c41b0a421cff555",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fac09fe19924cceb58c5ad65ecd123c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7cb717a21c1f48a9b18166ee33995dab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfe965cb65fd4bf08a08669bee3f8362",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bd22eb9cb874146b21e55dfe6080dd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6139a4f089546c0a5577ad03e551abf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9fa4719cfc5475fbbb10131487ddd75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82ae4b8ae8214457adabc794cce84ac8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1d78a9843224da7a527ba9b91036004",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2474e6b992f4ce1ba3d90a029cc484c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d82eba50a6784cab83770fbcf7e36f83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8630f72c6b848b7b5f2cd2b9c195c6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "592d9361635643159ef21aa306e14596",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "493055f25c0c4be6833cc4a3d3380b10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "072520bd1c624d8b8abf62ca3ab30f86",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3be87375a8b14df88e2502804305d012",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf68ad7d516e4e04a4d0a3fa302ad3f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "293bcf9ffb6c4de4a995ae50d1bdd916",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89bbf2f0e3aa44feae45e66ed0acffdf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0620f963f8894091af64de80ecb47bc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43295f54d4d04460b61d90c9e1a9301a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adc39d0768f04b6f917d6e706b5ef72c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c091fc436aa4b8bba2a34be1ed0753b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f3671d3f1764fefa98c5bf91e1e79a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "775d27002e3049dc98f30dcf67cb6f40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b186f0db81f64281a44b48cccc4e83db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33f20b12bd13494a914b2e4bcd3fa2a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e71a5065ad014d9bbbfc1986cb87a32c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0c6ef87d4514a0fab1084dfa984cc3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a442196196fb40e0b92b0ed2d4d63a7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5316152e12744f88ccb26d4fcf258e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7e8cc65b0a7441a882345ef331318fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2b29e9b2e8e404fa93889b30db947f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86b2e3ae3cf6415e9d1d1cf209279ab3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a99d8696ae764535bccef2bb6bad1ff5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d318a12cc8d40d3a7b4c10fdb745234",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61f1477abc6e4deeac719bc951001762",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b48468e8592d43d386876a65bb1d62ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "247c690256024dc7bcad022ba108832d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32e6a3de314542c58994f9446bdd6073",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87009da6cf804ae2b58a933d41fed500",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4ebfe4c28c74ac7bbca38d4d746cc06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0aa136bb20f48159407a6f45776750b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78ce71126f03408d85e566de73a7600c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9b99cf485264694896fabb37d2390e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be9111dad31a48598982be1e5c2eb78b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4586fbfea8940218a25bf2d69244d1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2dd0c07474304fae855116fca6058e26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70c6498907b1486bbb5ef98ebf437331",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d6e6bca29c4475b85d0807509bc25ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "260640e9f4ef41059c6a6de476a277fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81e0c22abf6a4b13b5d9972e10f3b6c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4178199f6bc440a9b7b825849acdcebb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b06dc446de5b4b48be0a08fd0decd21b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "efec783d43394f32ab7fab108415502a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6aed921cebf4589b1dce1b6700eafdb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01f263cd85294013ac5f8c024f10d03e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ac652d4ce294cf69758996150bc8cd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "08f6d0db29114be6b0e6b271f494d202",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "619112eb004d48a99a977e53b7b3d9e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb4e6d342b2a467b8f469437dadfe0ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b48d94d971d46ec9348697566b9f80c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fca317a46c84685998786b5d1459cdb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b6b3f4969594b6891f8b8a26c4d010c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "297c8d0a23884e5496f1f42c72c1f87c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "847c89852a3247ebba36c7ddf505d4ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "306ad8ff581d4bb9a018e728927321c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fff37075dde24cbca278ffde55551545",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24fb8db066a2416c971c8ac40b1df2c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b0cb88eeeef480d802c616b3ba4a461",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae1953b4d8904aaf9ab454d0d41829b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68fc71b9281c471eace3bdbec311f3a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22a45db07e9848bca651611482d916e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93b40592507543eb9b0abcf641cb7d2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e5b471362b74245ad786b19b9b73317",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac528ce4fffc45e7a5bb26adf5652678",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64b1c63cb6eb424d94892cb183d3f3e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9aa4a25f9ca24307a7cf2a0850d9db27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d01d508de924d4589302291f96569f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4154f57df74d4f08a003f5991ca7c579",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f9f73dd8de4456b91529be1772bb214",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04d2093d14234c1e8aa4d0bb02ae7adc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5097d15041eb4684a22135c7eeaaff80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d043d205c404485bd5b5c6307376dd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23d00e1f5b4642768423aa509aaf147a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "937e1612cfab419698e416a7021db612",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3dcba7d42bdb44f0961d2ba79f0b8a5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "193b76fa3e174406a3841bfb45856944",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "963f4478aecc46b284328b5f4d751864",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c285875b78c24b8e834cf25489cc9b7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60538d57e1c0455d835a73969ffccee1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18941fb5dc4847edb25c4612d2e77d90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2f2a31828a440f0abb4b89908d32a23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb730a97924041ba98b6c2eeaf987c87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5d606822acb47998120665e5ae27edf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6d84b1609614d67bdb13f4fcf8a41d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3affd037843c4e4b96d543c004c3c65a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2acee8f3c6d4a948aef3e58f50306fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ac2f7e66d01418fa3ec360354ceed75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bf52fec65024178be67d082e27a23b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99a7f2e8c1c040a798645254ca3c1818",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5c1869d31ed45d7994c3cce5db4db20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e508a4f46ab74728b172deea39e5b2c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52e799bc54514b478738a3d951beba87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e5bcc33941c44c7813918dc1c58bdac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c61bf87ef344762b421f490edbc8037",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aef19dfba87841dfbad78be19c86d9b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9917397fb24940bcbdc48db217bad37e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42608a5e79e741e39e2c3bd3ccde3978",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "006611358c8343b8b04b13a6df7dbb5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65efb37f5bad47b693aa82f768323c4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76e28bbddfd443a3b678d0582e507e8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ef66fb203fa42c29e081fb3095cf097",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67ac037cf1b94757bb763683d7278d9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "caea13f7cdb94e2fa02f584e199b1216",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6b97f3318f84e76b431584fe3a8b456",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd878502acd44872bb0345920f750240",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67dd9b2cde4c4a6292c4f16bec78445d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac8696ed6d3a48d9b05212e45b113745",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "818b1825fc6a423495bdafb8f60461a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dabba0b6b24646688c923c761e27deaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29c8251df8c84b5690f05e49cb77ad8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89b6560d73864d14882005843bd47866",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a477649b4d3840d7be19379f7eac316e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e07dee21e1b04f2e852ba5653790f591",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b7de2901bf0442bb1aa564a6b89b9c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b070aaf11c2434abb02462761c82e6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4071816fce8402fa4a9e80c2ad4e5f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4ae73620ebc43bc9881000447906080",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a71c5cfdc1b64aeb988c8f8d8ada3e5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b101dc66aa741308992e7dc98036547",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48428ff1d6404d0489bec474c190ac8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd82f9d690f14d6aa9824c041341e961",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19946ee2cacc4fd6af210535028b8171",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60d30287e1f54bd2a3717a04e5b9612e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c88f60c9352943098d9ed51eb22717d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98dc517a01704822acf95f5d993c12bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c6a6e7e7c7f4bb08f0113182a9aa95e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f3e93d62059486a8b0675a359493bed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f35702937394e8db87a60f1dd149679",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee8f23392d71488aab01b561251f117c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11d79b097c3f42cd8fd28c153ae8caef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1f6cfe10bff44839c2631e90a62fafd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a10b93540556456d87295b2afcc0d0f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aaa2974f3eba4a7eb43f2598ec1e4728",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e824332e6be346cd961d369f60215d5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e981c9dfc7748ef9992ac500da6fa6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dff3700b9cb84edd8dfde63d0343cb0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fe98ca171124fb1aa757ed71a731efa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5853833bf5db446d872e86f22bc14b90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ed7dabd9c604943b65835449ae6500e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "551a35320b814f859fc8c8711a517bb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0b84991a6a44c598adee4332795cd71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48a2276a528847419263fc33935fcb57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "064e8f61b8f84c3992575970c5750de7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b56116e402548ac839ab2bdca001c47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d253f1ad9974ad0ae86846f96c808c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43869377d4eb47a29df778be273b6829",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "078ec4acb5dc493a85ffed4b3ded6424",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f91960c9ee5c4e699f90f2b814907ff7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81e27e0e7be8427fb58119867b36a369",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "998ae6514d9442ae969b8b0bc67d85cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eef94b75cafe4952830492166a791946",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0a30836893d42d894a6594ee9d26e6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa22124ce7164a9bbbc2b619423ca05f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b71f08ac64c04b6f9a574a360865de6c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32910251cd9e48008abf694d66ac4f88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8c0974e5448408f89181f4b91f19714",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87e0a1b9b6d54efe91e54da235dabd49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6435cbe504c04dafa257ef3a2769f93d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bdcd5f4ae154cc694892f37157c04bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d962e6e8b6f240b59f0ab95807ab373a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05d798d6ea4b439a9dca28b0aaf98f17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "57b10976e82e4d3bbcae3c794b181cc0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cefc3704d9a427b9bcd47c10d22de6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0cebc61fd91432eac10d2c2c049d9df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3485a67566ed465cbba37e55ff3ad838",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9d51b901f584f5487032583a4029ab0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2416419187ca4fa493fe7140167fc62d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24f22853459d4eaa81cbe9183ae05d70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84258b157c7a421b9faab440d7621cb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "945bf2bdbc4540d5ada5e5d677019b4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cc7edf7bfdf45b58ee58d91d136808c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04287ae49bb34745bc0afaea042f12f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03f986e8f89e481b85a36acd6afba818",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1912d82f0554f63b1858cec1e9f92a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0acb47ea7e91415eaec2c1a79ed5c0c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "388f0b614b4745ceb49191c75c1fdf96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ac672e8de0a413c86f34dc8c8d80838",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04d011e137c54f9ebf1e9f10e54e69fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be72cd29aec3407096f73f7d648479d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c2b51174b8b4a4380f05e13e8bf2f38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a31643e4c6274aaaae53aedc9245fba1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "167761bbfecd48d5a2284189a0b6f5a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af0a33a7c00f4017b9140092221f7f5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00dbe01036ce45e1adc6156bfa0a5c88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7e625b4b68a4bddaa0643548a096458",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48f1f87f840e43bc870f82d07a2b9808",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7eaa6412aacd4bf0ac0593fa42e62628",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fffb42ebc4794a62bb27f737dc1141a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f3c87e63ce94b64a1a69a22b3219400",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8bed08d903e4b2280b179124cb35aae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3303627f01c4c13a8ba508efb2c1f4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83fe4c66de614c48bf9b4e743916ff48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f32b7d96a7941ed85c98fc1a83c4c0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6562af74e94d49b1bbc44874239ac36b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac053986d1334456875b817023053365",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f52358c03e949bca4c77ba7fd755157",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ddb246b4b2f84d24ba7d89c8a671acb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbaf10f1f9054605aa21c377eac1b3f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91046ba47ccc4137aa7cd2ac5615a885",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a64e463c7a7414590ec9083c11fcd01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17c837c0fe8a48279dbc5a284fe9a85f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "882c8d07fbc54b33955998308bec8e79",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9b60bbd58a74c039e94a9f4c8e2f270",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "172e05e53bd64319a7d9d56c53d38cab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df892f02d82645c3a133a9c54c8920a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8acbf108ba4243bba7c4c94b1be9ffc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb2e059ff7284160b2e5fe12d0407b17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ad7ef705a3144aa84a4adec2a61a177",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "929202d41ac84c89b2c95e2dab433b59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6e221dc884f49be9fe85d8849ec10dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d22f45aa58d349adbe062a1ad4adc289",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee2b06a2aba34d86a3b54a52038edc96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a33b6fa4d744c6e87eee1f70b38faf8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f892651b61424d77ae4ebd12bfa403e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed74dc1bfb5a49d3862422a26c9a0e7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cda531f4e0274d63b119f5196e1e8ec6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab2e747f8165488d844ee67b43fed5e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aef1f6d649774f9d86f65765a2b1cf13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b18d98f40d841a4838d7c94054f36bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d64a0890c5b14dda94487207684f7179",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55ccd039daa6405eb99feb74f4899a04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d901e06e79a45c0a0a44f6c872192d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f884318480144f9861cdb242fb29d9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "821479fe65b04a60a6ac01ee509a3bba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c6ffb5b89d24794b59ffcec44469511",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8247d21fa2ff4443ad327ffc09869c82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9dfc9eb5e7df4627b51353c2bd9e3f3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ebc03689a3b497198fb00fd7d45ed6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bd6dd978804401c94481e59347ee10b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6da50b918ca1461184c8b591cbe337cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e90b1e964f6d47d8a4fd22639bb30e53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3437230d4c5486d91e0050218eabb9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b048d0814b5d4de2a1cb0deda6e6f689",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11e7496ee1264233af24727f11459d0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03783ace34dc43deb754fbe7390945f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fd173a40709403f9409de4d59739a15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7eaae0a35d2413b817bd121499fbca8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b88eff0a25a4bd9bbbf099dc93b5db6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55a8e63b388b4126924db9d09af0b69d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bb60fab19234a76b2755804248295d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3389bcc74134b57b7bc9410d8ebf46b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "267df1a0f0ff4329b5b1833e5e252774",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1f4b5aba0604b78bbd942ddb8589b1a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ca3a57f6d4546e1a1bac77a6154dab1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14a660b7f2e94df8ac7b73fb52a098d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a39da8a726ab4a4fbe472b2ca8638678",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "627f1074879947008caaa86f5cb5cc12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7ef842b949a47238801e07a1eeab54b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d9af4c1b24640febee4fcc2d4f1e452",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f17ba214a5a4a8f80b6e787e6f3ec54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cd7ec8c13794106b6d5175db7cb9f0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c1db98c77134cb3970f9f88480d896b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13974dcf6dc04d0f9fba37b9f241fbae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db6d51ee6f2549d5b06fbbdf7eaffe5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca4d4a7716be494aa295b8e94f2c2da5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "784353f33c664496b05180bf70693a02",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "718ac8ca60c84bdab556577e10b22312",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01ba3c72f8354c4788fd7ba2e1a476c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a72e16249a2d4a1a9c1a198a3d3d1c71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "058a7148ba3d47e19fb7bd22a220e22c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78cd0fa716e84498a8e675609d7f6d0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3cb929f38c40482ca939450a8881141c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f5337d77e1a46aca2e25aec332b8d6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7dfd64e6108e4ec4806018dbb378762a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "193529fdf5d349e2baed836c18354bc3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28bebe728f16417a899a1d7d10ee7fbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93aad48cdabb4a95a4f3b7e48a02acfe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ed8656efded4a35956260a92ab8b923",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "051d453b1aa841e8b50eb4e354c091f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "568f479861c94b6d919bb4d7aca8b910",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41af5bfc371e4633b97afb45ff3fcdd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e06ccc7f0e2841679c291e3c960a92fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ec293bae19d4cb7a493b2ad1de83658",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c69b2c865ce4dd3b23394a7f58807b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c7df1ad30db45a7b108900eef05abbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ff94f32fa3c4e97920cfacdef08dd6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74aba1121d8c40f880bbd67a647cfa41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99d5153f17674445a1478f228b078b2f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29058e68a67f4add9dadae1b1613144d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "944bd3938a104cb5b31fcd5fd4332191",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f76eebcff18492b9e86241eb2e5171f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fde1699453444b4ba86fa16415f45f86",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d5afd9cac194666a0b6201629be8923",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58d9a4da96324f848b58e61f87334e13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3365cfdeba734e3abda6b346e44791a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b6cd285ae824f258bbd45a04e538176",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1febc79d42d746d2a6038d256b008899",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f1ace18b75146b5a9e8f12da4092a74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "161c8aaf327d4ca0bf6867bebf636f4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aed2417819ae47019e377d01b0c7b137",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8d467d007da44fc8e303a0e2828dd72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b02d7bb7bdc4c7faef3360f17d68b5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2f1a73f5b79411a88a168496fb645a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2af7cb3973a24c3f8e56bbb64e5bf6da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4defd7c65df44e04b0ca86f9daa47247",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6b62a6228fb4b28b1cf58699c4b6f48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c79a819077d417e9b7fd97663c28d14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79f09075c56740bf8c6dab1c309e341c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73934508fb8642bab98d11f317ddd68b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f31dc59bd75439f9c274446fc13924f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3782c11dcaf34b4e8fb39d2761ec58eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca0a1c7e222044d4ba7fa6181633b378",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df48c56700334d6790d3be8f3b6b3357",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "153ab78f24154ba48130404473b4e8aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db0aa23a12af4be3b5cb941b5b93d75c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "995fcb64c91f4304a56552f2d2306a98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6aa4fa509ad4873b540f70441c9b5e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec7afc7d4eff478baee89b1e6236d9c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63c2a3bae6d34132afc632e8970d6240",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63176a05777b4d90ade1ab7b20167b8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "890f52d11ee94e368110b26b458992ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d7c7d57a9f74d129e736b42e2e99f9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5c2c98f12494ddb9fc00f16a1f84d80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85c27a3009284192a5d8419d5af89e55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4b03cde4e1e4a6db29c7903532c05b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fec3db8ee87b4fb9b06db8c90841f7dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3563651b55a4d0a9431451d1cebdea1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d333c8f34503472f95e36abf1b53fe60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e110b0d5d9b5402e93ab4efba368dfb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5004c154d5e046bc92efbea10272c800",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49e4283935fe4391bce4b99fe1f5c0e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ba7007f189c479e9fb944e5803eaa59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc6876798dcf40029ca5067e1b485517",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcb644b453274540b9ab5c5d6fc8c8d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65b61d7c051f4bb5b2c358c4b23ae0b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1941155e57e945219946ff299b2d5baa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b302b7cfb3284007b3820c9a9b58df90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e844d1c948fd457899ef6c809b869b18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a2383c11c3c4472876557514963bfa2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6246dbf8700e4a2c9feb34526034fd51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd6eeb024440429a9a98f106a45e0743",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07fa8e9779e042618fa409f46cca8c32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0feedd2d56a48cc87cdbf513b2355ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "797df91b1c384a24ba255b2698de7824",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ddfbf21cf2f048a1863479509a2b9668",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fca7320525c473b84726e6f3cd4c08e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce00f72a8f7247c08d6fe5ff53229c6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55dbcaa4a61d4d7897331cfe0f26b74c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ab44b3bab104533ad7b63639898f335",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "613ef173471740f585e4a289118ec2a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a363b55adb574113be8ee16e00cfe1ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fd4cdb73eca4554bd1bf3d51b22a6f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2218f34ab9a6496e86087b36da29ada0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b618c9bd3dc047538675fa17b08456c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9266803f4cd4bf89913387ebb4b18ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "591fec3a7e7343368f22738f3fc3ebc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b47e3614440542d3a1bd2698c426ef8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a430b7bd10934a77ace038fcb77cc8a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5bb7503361c14d7cb1203dad29cb0830",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f037a4dd35bb4eaaa1aa137f39717ed1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6dabfa70ee2c44528c0008446c5f1509",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e50c5fc2d5d349d19b41be141f9de9c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a38293bde9004898bcfa14dd53262201",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e32dac31a1e24b43b347a514a4fcf2f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "839cb21f93db4a8f8c03b5f2c52be18f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36b4decbde9a47aea3f849fd14839037",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "839a4f69c30b478f829f53fe89eff938",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07c526ba5f8641c4898010020e03b2e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c323dbaf4f340f58c5a707a2b273702",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70ec0677b93d4d479eeb5914e60b44b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c88264ff49f434ba1a51a534e55f9fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77b4379d973d4138bdc163ebf4de14cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3199d31b56c47a6b56e6bb6de07ff0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df48e3fd796142c08a69960920717a5f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f3ce19d6f034f77b19f5b6ba69116ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8821a5b8036f4c3189dfaef904d5a6a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7df98ac887134428ae679fd7999077ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9f7b3332fee40d6a604f3b6fca51214",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f747c0afbb304e27aac3114a852a9737",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d426dc9fef94afeb46f9a626a450002",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2993d371a3b7415ca634d0d68cca2b0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a21365b316ce4a81866e52ef03d9e5d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6667bcaf6fad4ad098ba51d1a6ed71d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b50711053ae44fde8d2bdab2e1dbc4ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8185a1c1c56746e7bb57e8f2d641f5af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6094d8bfd0d41b2a512bcafaaf7c896",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04361627c8b8483184b0cab94b792ce1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32eaa16963b44b1a8a8c467b1a6d697c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4472e7656e845d09bd25a6090ad738e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85b43eeb5e25473eadf247b40ee15ab9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9857868f938845118163b97cf97691c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "752c52c53b504f11ba0d95eb7544f8c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4639ca665c3049b285e74c83230990cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a4d0114f670430eb0e9198e5b4b243b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bafc40bcc02496eaf83dbbd0e442a67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4652551d922f432abb53407e3ff8de06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1dc0afaca55547a09e08e8325b92ba82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a86ac2a5ce840d283889a33ab62d442",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a9c0760ac7b4bc8a6f913337bd343a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "837d1e5b2c4048209a2fa8294a4c4c89",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f0d76e6cbe8427eb963ae72dce7a362",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1077a7718290478e859d00d1aba14ec3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef68acea650c4dac82ead704d241d617",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "646c7fae97d94145883b2fb6de5b4d36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d651b6683e6a416ea93262e1c64ad392",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce1849e7a8c047f89b2a2fcfbb017a96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cea48ef03f2248fba46cc73f00aa55eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "713e12e8873a4288a7a386377f20b237",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54f33c2abb464fa2b60aec2123faf3ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5846494e926c4c59aad305d5aa2d5ad0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8a653cc897342f99807650a256ea263",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4dd3505204434752af244abe839de8b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54d8929768394fb489855ef74f8577a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c929480e32a845dda10d6287749d93ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c36200f4be14ab79fdc3458b0011a4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "580f7baeb4704030b431843cdf5447ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7140eaf0e2fa458e8f21ec93456800fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d4492f201b345c2a665a39a3c02c48f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebf65b7098444047b4f2141b01000693",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5f1312b03914aaf9c8d54cf29714185",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65dcfca545a6421d8eb997febee8ccec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2a4084cb8b248a0832d117e3d3d55a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e0a07bb52ad42ad95e13a8010953e21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "542adb8bfeca411da0ca7b040a8d8dd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d0d731fea0b4514a32ea02965ff8e3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d0d9cdd38024b5a8a9847ba1eeedc30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd017f7b3f584b788829682b5b855e7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ef5b587eb194b57a986dab33ffb8617",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e6e7bbe0f3834c89b5926baf57b645f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d00f7da2491a4307beb1dc03dc341432",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "873b85538e2b4f1bb236bc1b30965594",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd34890817f046b384d4ce71d45838d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c431d32611cc47dca126cfa6e91eaed7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52b3f70a24284ee4a55768528a78f8f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "809b7894afe34266a8dbc3aa25437b49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a24b9884a6254acea16d0a499b22478f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5fd994e44d44ddfb954c9fd45b67d85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21ff2d78b11c4ae38f6c43ae24ce18c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "123b19c013854240867131ea2fea0766",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c38d5f07e8c4f8189ef2b50160578f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0b5604d769e4401bb0ddee3a1d2fc7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "391a1db7e1bb44cc8e3f5d7a72cb5e17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "345321fa1ccf486fa787da07ee798df4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fe85e26f8da43f6a216da4de7a2ca18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b9e9bdfc68c4da28394f6a58c14a10b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91af1738d0024e99aff3848456c5244f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97bb248f293249a29e4fc371d4fbb774",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99d041ac161c49579dc00a6e4fb5af90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0c876cf00c84090b8c070afb8ad34e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f20ae33b5954e76a5711b4a222777ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "516f156cf5aa49fdb8ab64ebc6d66053",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b855f3a0ebc43a7a5efb05c7564a7fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8cd24810ee54ab6b4a16a4eaef8ff53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "168b0f994dc845fbb682e2e2a8ff51a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6ee9d4328624c03a0e0cc83d4b61630",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b33419db282416996972eba0a9fcf7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e93e0a34ebfa471a87106b0de0c89d92",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "844f078f4c344dfebafd19842608b08e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d94cf906443148b6a9a1949dc468c790",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a876ca095450471ead5975458b9e40a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d5e3c3d61114d4ea9fa17034ceb0cdd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "829c6248297046258593c341822a484d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd82548904ac4b678ab3303397fb28ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dce0110e028f42dcbf4ecdadebf1afd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f7b97f440304c0a9c5f2f22dbd0b8ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f2d254c3f5d4801aa4247d1b8007f07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc1715ad35044e14a1e259b8e73c7f5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b8642c30d1e45febdfe2f46fddae9dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df829bcc8f404728a4c7a22d1243a1a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db17033be4304d4e99fed1792dd383e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56c26a23c8184ffe9a46bb8561113eb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce19e4ea0cc44da68dbbe96dbf25e160",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00cb78644a454b7884ec6574b3c5a68e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db331db96a4448059a3dd6db1238e6c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a2d92b181454a2187a9afcd220ead43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a238e22ad09467e93b51efb62758791",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e49c56fb7694f1eb6c4fd9a4eb0926e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7eab0b4ba1f64de1a5d2afbbb458e082",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2105eb5a0e4c43d8b2087b67e5045a87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c462d30aa7b4cb7be07022f8169f103",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fe3856c09604f55829873213f002f3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc6485772a6c4c3baa59fc677156effc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37914f2bd4074fda94e7e23931f3bd74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "312ee9176f914f7e82dfa020d79d8493",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf8e682b79ab4dd7ab47e9c785cde603",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19731e64ca074d30a3a6c3dd7097d796",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9a643d2610349d1883f1eb43067f6c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1aac12c0763e40af963057d15ead7100",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "508c01d37a264f45b7b3edb896065a59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e30fdc0e5c84642912eb8f0e944a8a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ab603764ee64f66886d4c1a79035dc0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf446cbf545046bcb344a861f03e89b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae401286f9ac44beb60e395783b781f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f818b1ab425c42dfac52aff0156ac83f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d00ccb33e3b416694302bf580ef2231",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6c28039335643aebe4f482d4dd009cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ad520cac81c400588e97fb1e82e40f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "753b484b4bcd4440ab0aaf874822f6bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01750a1dea4f486b8c59d71a0853d492",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc43c20572c24a7bbd6e7004d9efd6d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c8c78a9f0884b6db7ba3e4bb6c238ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "881d93d16c894ba7b31cce8d1eed994b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48f950b118c34462b5f5de678d6bb2e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae3b30a5c1d94212a4fab46d3477e5bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75ba0789c901487695c3e39da1b81f62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80a1229ac23e4550be1538a6aca62bb2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "757a5b98eb5849e5973247f7792d7892",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5616fb23b414b3fa54234a715054f8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9cc6ad4b4f2a4d079f473b5a92e2ff4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53a28493c0ac4812a42c9190436b944b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f66239b927044ebd983fccc4128d9b5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bce816c80ebf4d79a9cef1badb9173a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "124f7e6eee5043d5aff24edff257b4c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c970cb9eb814e7cbce1ab4fae49bec8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5281b1664b84c25930e089f1b018dc8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9510edabb89747cea2f31db2379e2458",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f0b31f2d9cb423fb25f3f4d20db3b42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa5a2614413b443b9ac4130932cd79b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ff4452015064b6687a15d6f957579f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e0ffc9d7c3d461db23ea628e3ec65dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "757dc167f5e140ab866fe4aec7a7a59c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5665bb2dc1a2470f8384dff8fef28d5f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2468324abf84133876d84442efc8d6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd05f1b80a264c40bac992c1892624c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95e29bae04f54713bac246449419e059",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f142c4a74fe14d0cacfc519dba3638e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f556cc954a64c8f847c211233e9952f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6b9bb7b4a504834a3bb38411b57c574",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "663d0b2188a04af79edd9552a5727871",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5594718053db4200834c98e886ac76aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "008298dbcc8e40068ad4ae8b37ec2c0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91eadfa30bf14447a7eda12c9fdcd84a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "879c90c1aa7542769e98af06bd7dc1af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b5d11349dfb45fead39ccf0bc095d03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffbfcec877464f8ebb4b07310abe46d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74df3ffb65d141289b3e5101ae1e733c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc33498a02f3493d822ec2807b101113",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48aa9bd6f19f4d2d8273c1f1cbcb2895",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e6ead818560431787d2526515fd97af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c78d8a4b1dc54d1685df43a719e3df56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73d3f65517d848bca23a9ff90196c450",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee2ac56686624f06a09fa87a7102655b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "077c08b71de24abe851279d28c051ae4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ddf22f0e1d5940699ec52393b289a089",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d152ea2ba854dfb80edb0e314d2dfdf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43602604eeb7415b895017bcfc90c4ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b0a09c6216d47d1b59450987def09a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0ac9e7026db4b85be53ed24caee2ac0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bee22aed4e644ec8194ba02dd86f825",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b54176d387354404bc685f5b059d219c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e553fe7f3fe4fb2ab59b3caad3e3eaa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2da0976b0aec450fbb1d55b6f78de4a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6909d5e8eb8048bcbae472be7ad1c74f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32fe961295dc441783b59b3e4339f355",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78a6668038f24603b89a36568ae17ac7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cba3ddf433804e1e84c7e64e60d7b998",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f2ad280d63846e58f0cffc9f4bb02de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "973895af7f2545a78f0f9662ae148bac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fc4988f33e5425bb6ea2835469a8507",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71cee3423be84503b3df3d1b535ed590",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ead0d014f6194eed9748fe6ca6124d06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2da4f6c669b34ab5b2c6882db8e1419c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8394454bac0c45e8bb691cf522771450",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f44414c7e882423ea7ef92f474adf58a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "56220afef4034aa7bbe2052501cc86b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74ccbd436c1747b2980b2c2e30573d5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df6f9954370d4611a7c224df5c5a6106",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "670130d21628456daae3fc08d9d2b131",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "698d7a9eef794346b7675c29176ff5a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eef91c619ee64a84b15a4e579f2bb932",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69d442a1ff384b19853a20e50d81b950",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ffd7034e22f4f8ca4a7dbf975a8ae6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99dc2278118e4c36af8c9ef7d5f7df05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed0a5be6239c44de98d545fcb07385e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75075937bf1948eeb197ca53c72ab305",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e9b6ade39e04bb4a426b760432fc561",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c5ed21a7f304a60a8fbec410623d2d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "873d0e757f574cd7a624a2144fa4777e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f09ac3abdc643f7b46c7e812be22ffe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8ce9433936343bba2ece01c0d79ec5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42a2a6434b6d496a8383d194db31dabe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2dd4ec7173f641f088cf20ae9f51b02c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "423a469f2b194de0801ff3f00f16578c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3c4b2b50bde44d29152eb2f58007dae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd443a5347fc48409fc348184013db79",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38d8ac067b2a436cb7060cdce366a775",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc67064224af40ba9cb3ce8d15ab6860",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a67dd3f0f6247b885220f2e1bd4389c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e033d3740a804f7a9b322f3425c4adc9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b777e214dc3a4db5ad54647959d91a42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c5ba4df2620417d982c00987062abde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb4040c783be4f99947d50eb7ed86106",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a0464059f5d40d68f967159d35996b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4150ddcd10c946b0b5ad5a07e620fc84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05f7c991028a4d19b11f0a144fd03b48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d1492f55f004d4ea508a88b5bd1e47b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93d2cfd0b3ea4fc7a8d855edf3e1b297",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65fa6f2eeb934db298a4ce6511f1cf86",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "356a4ab5695f4c139335ca1d6916876f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eda85454e89c481bba759be79ace331d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "name": "stderr",
@@ -205,13 +70226,6 @@
      "text": [
       "`Trainer.fit` stopped: `max_epochs=5000` reached.\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 15.82it/s, v_num=7, val_loss=0.0333, phys_cond_loss=0.0268, train_loss=0.0268]\n"
-     ]
     }
    ],
    "source": [
@@ -235,7 +70249,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fbf98ca7830>"
+       "<matplotlib.legend.Legend at 0x7efb072ea120>"
       ]
      },
      "execution_count": 5,
@@ -244,7 +70258,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArQAAAKTCAYAAADol5tUAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAA+7lJREFUeJzsvXeYHOWV9n1X58k5B41yAAUUkEVmLZAIBnkdwAbLZjHY7OJ9bbwO+l4v7IK9rL28bLBZY2NjwGCDExkLsLAQQUhCQiin0eQcuyd2T3fX98dTT3UPmhl1V67u87uuuboYdVc9gzRVd526z30EURRFEARBEARBEIRNcZi9AIIgCIIgCIJQAwlagiAIgiAIwtaQoCUIgiAIgiBsDQlagiAIgiAIwtaQoCUIgiAIgiBsDQlagiAIgiAIwtaQoCUIgiAIgiBsjcvsBZhBNBpFe3s7cnJyIAiC2cshCIIgCIIgPoIoihgaGkJlZSUcjplrsGkpaNvb21FTU2P2MgiCIAiCIIiz0NLSgurq6hnfk5aCNicnBwD7H5Sbm2vyagiCIAiCIIiPEggEUFNTI+u2mUhLQcttBrm5uSRoCYIgCIIgLEwi9lBqCiMIgiAIgiBsDQlagiAIgiAIwtaQoCUIgiAIgiBsDQlagiAIgiAIwtboKmh37NiBT3ziE6isrIQgCHjuuefO+pnt27dj5cqV8Hq9mDdvHh577LEz3vPQQw+hrq4OPp8Pa9euxe7du7VfPEEQBEEQBGELdBW0IyMjWL58OR566KGE3t/Q0IBrrrkGl19+Ofbv34+vf/3r+PKXv4xXX31Vfs8zzzyDu+66C/fccw/27duH5cuXY8OGDeju7tbrxyAIgiAIgiAsjCCKomjIgQQBzz77LDZt2jTte77zne/g5ZdfxqFDh+Tv3XjjjRgcHMTWrVsBAGvXrsWaNWvwk5/8BACb+lVTU4Ovfe1r+O53vzvlfoPBIILBoPzfPNfM7/dTbBdBEARBEIQFCQQCyMvLS0ivWcpDu3PnTqxfv37S9zZs2ICdO3cCAEKhEPbu3TvpPQ6HA+vXr5ffMxX3338/8vLy5C+aEkYQBEEQBJE6WErQdnZ2oqysbNL3ysrKEAgEMDY2ht7eXkQikSnf09nZOe1+t2zZAr/fL3+1tLTosn6CIAiCIAjCeNJiUpjX64XX6zV7GQRBEARBEIQOWErQlpeXo6ura9L3urq6kJubi4yMDDidTjidzinfU15ebuRSCYIgCIIgCItgKcvBunXrsG3btknfe/3117Fu3ToAgMfjwapVqya9JxqNYtu2bfJ7CIIgCIIgiPRCV0E7PDyM/fv3Y//+/QBYLNf+/fvR3NwMgHlbN2/eLL//q1/9Kk6fPo1vf/vbOHbsGP73f/8Xv/vd7/CNb3xDfs9dd92FRx55BI8//jiOHj2KO+64AyMjI7jlllv0/FEIgiAIgiAIi6Kr5eD999/H5ZdfLv/3XXfdBQD44he/iMceewwdHR2yuAWA2bNn4+WXX8Y3vvEN/Pd//zeqq6vxi1/8Ahs2bJDfc8MNN6Cnpwd33303Ojs7sWLFCmzduvWMRjGCIAiCIAgiPTAsh9ZKJJNrRhAEQRAEQRiPbXNoCYIgCIIgCCJZSNASBEEQBEEQtoYELUEQBEEQBGFrSNASBEEQBEEQtoYELUEQBEEQBGFrSNASBEEQBEEQtoYELUEQBEEQBGFrSNASBEEQBEEQtoYELUEQBEEQBGFrdB19SxAEcTY6A2N4dGc93q7vwfzSHKydVYTrl9Ugy0unJ4IgCCIx6IpBEIQpiKKI7z6/Hw++cRThKJvA/ecj7M8WluXiudsuwaLyPBNXSBAEQdgFshwQBGE4oijim3/ahx/95QjCURHrZhfjwb9diX+8bCEq8zJwvCuA8x/YipcOtpq9VIIgCMIGCKIoimYvwmgCgQDy8vLg9/uRm5tr9nIIIu3455c+xPe3HgIA/PKmj+Hv1s2V/6x7aBw3PPo2tp/sgs/txO5/2oClVQVmLZUgCIIwiWT0GlVoCYIwlB2numQx+9Bn10wSswBQmuPD63f+DTYuqcD4RAQ3/OptjATDZiyVIAiCsAkkaAmCMIxwJIqv/f59AMDtF87D31+yYMr3uZwOPPGFC1CRm4GjnQH84x/eN3KZBEEQhM0gQUsQhGH87O2TONA2iMJMD/7tEytmfG9Jjg9PfekCCALw6M56vHWq25hFEgRBELaDBC1BEIbQNxzE9146AAD4wSeWoyjbe9bPXL6gHLddMA8AsOWF/UhDyz9BEASRACRoCYIwhJ+9cxKDYyEsq8rHbRfOS/hz91y1FD63E++c7sHLh9p0XCFBEARhV0jQEgShO+FIFD996yQA4J8+vhhOR+Knnsr8TPyfyxYCAP6/Fz9ENEpVWoIgCGIyJGgJgtCd5w60onVwFKU5Pnz2vFlJf/47VyxBfoYHB9sH8eyHLTqskCAIgrAzJGgJgtCdH795HABLNvC6nUl/viDTi3+4ZD4A4Cc7Tmi6NoIgCML+kKAlCEJXPmwdwI5T3XA6BHz1ovmK9/OVi+bD6RCw/WQXDrUPardAgiAIwvaQoCUIQld+9V49AOCTy2tQlZ+peD81BVnYtKwaAPAQVWkJgiCIOEjQEgShG9GoiD/sbwYA3LymTvX+7ryENYc9sfs0BkdDqvdHEARBpAYkaAmC0I33GnvRNjiGHJ8LGxZXqt7fpfNLcU5FHkZDETyx+7QGKyQIgiBSARK0BEEoRxSB8UH2OgV/+IBVZz9xbjV8CprBPoogCPjKhcyH++SeRtX7IwjC4ogiMDYAiFGzV0JYHBK0BEEkT/s+4NebgP+YA/xwFvC/5wPHX5kkbOPtBp85r1azQ392ZS2cDgF7mvpwsjug2X4JgrAYre8Dj10N/KgO+Pda4Fcb2XmGIKaABC1BEMlRvw147Frg9F+BsX72vd4TwNOfA56+EYgwb+vupj60DIwi2+vChsUVmh2+LDcD6xeWAwB+836jZvslCMJCvPZ/gV9+HGh+l/13aAho3gk8/Xlg9yPmro2wJCRoCYJInGMvAb+5AZgYAeZcDty2HfjmCeDCbwBOL3BiK/Dq/wUQbzeoQobHpekybpIazJ7a0whxGrsDQRA25YMngZ0/ASAAK24C/vFD4I73gJVfAiACf/4nYMd/mLxIwmqQoCUIIjFG+4AX7gSiE8A5fwt8/ndA5XlAdhmw/l+AzzzG3rfn58D+p/DKkTYALK5LazYtq0GG24mTPUPY29yv+f4JgjCJjg+BV77Jti/7/4Dr/xcoqANKFwPX/hdwGbthxl9/ALTuMWuVhAUhQUsQRGJs+1fWnFF2LvC3jwBOz+Q/X3g1cOl3AQDiS99AsLseDkHA+kXlmi8lx+fGdUtZJu1TZDsgiNQgHAR+/0UgPA7M3wBc8k+T/1wQgEu/zaq2EIGXvg5EJsxYKWFBSNASBHF2Wt8H9j3Btq9+AHBMYyG49DvA7EshRIL4l6yXsWZWIQoyvbos6XOrZwEA/ri/mWwHBJEKfPgbYKCBPfX55M8AYRqJcsV9QEYh0HUIeO9/jV0jYVlI0BIEMTOiCPz5WwBEYPnngdp1079XcADr/xUAcJN3Dz4/K6jbsq5YVIEMtxMtA6P4sG1At+MQBGEAkQngrQfZ9oXfADIKpn9vZhFw5Q/Y9vb7geFu/ddHWB4StARBzEzLLhbT5cqQxepMRMtX4KXISjgEEZ8b/o1uy8r0uHCllJ7w/IFW3Y5DEIQBHHga8DcDWaXAqi+e/f3LPwdUrQLCY8D7v9R/fYTlIUFLEMTM7JEicpZ+GsguPevbP2wbwHf9VyMqCihp3gp0HtBtaddLPloStARhY6Jh4K0H2PYF/wi4M8/+GUEA1t3Jtvf8gvluibSGBC1BENMz3A0ceZ5tr/lyQh957VgHDkcq8bbvIvYNHTMjrz23Cg5BwAetA2juH9HtOARB6MiJV4GBRmYlWP13iX9u8XVAbjUw2gsc/L1uyyPsAQlagiCmZ9/jLKareg1QsSKhj7x+rBMA0Lv4JvaNw38CQsO6LK8kx4cL5hQDAF44SFVagrAlB59hr8s+B3iyEv+cwwWcfzvbfu+n047gJtIDErQEQUxNNAzsfYxtr06sOhsKR/DO6R4AwOLzrwIK5zAxe/g5fdYIsh0QhK0ZHwSOb2Xby29I/vOrvgi4s4Duw0DjW5oujbAXJGgJgpiapneAQCvrNj5nU0If+aB1AOMTERRlebGoPA9YcbP0B7/WbZnXL2OC9s1T3Rgap0xKgrAVR54HIkGgZDFQtjT5z/vymb8fAA7+TtOlEfaCBC1BEFNz9EX2uuhawOVL6CPv1LPq7AWziyEIArDi8yzKq+U9oPeELsucX5qLOcXZmIhEsf1kly7HIAhCJw5IInTZDazRSwnnSoL26AtAJKTNugjbQYKWIIgzEaPAsZfY9qJPJPwxbje4cG4J+0ZOBTD/Sra9/yktVziJDVJ816tHO3Q7BkEQGjPYDDS9zbaXfkb5fmZdCGSXA+N+oP4NbdZG2A4StARBnEnbPmCoA/DkAHMuTegjoijGBO2cktgfLJN8cUdf0K1p48pFJGgJwnYceY691l0M5FUr34/DCZzzSbZ96I+ql0XYExK0BEGcydEX2Ov8KxK2GzT0DaNraBwelwOra4tifzDvCsDpAfpPA73HdVgs8DcLyuFyCDjVM4TTvUO6HIMgCI05+Rp7XXSt+n2d+yn2evwVYGJU/f4I20GCliCIyYgicEzyzy5O3m6wqqYQPrcz9gfeHGDOZWyb2xg0JjfDjXWzWXzXa1SlJQjrExwCmt9j2/OuUL+/qtVAXi1LVTnxqvr9EbaDBC1BEJPpOcqqqU5vUheaWENYyZl/uPAa9nr8FS1WOCUbFlcCINsBQdiChjdZxnXBbKBorvr9CULMdnD8ZfX7I2wHCVqCICZz8nX2OudSVl1NkDMawuJZeBUAAWjbCwTaNVjkmfDGsG0nOjERiepyDIIgNIKfZ7SoznIWbGSvp7YB0Yh2+yVsAQlagiAm0/Ame51zecIf8Y+FcLjTD4BFdp1BdhlQcz7b1qlKu7KmEEVZXgyNh7GnqU+XYxAEoQGiCJz6C9vmKShaUHM+4M0DxvqB9g+02y9hC0jQEgQRIxICmney7dmXJPyxvc39EEWgrigLZbkZU79Jth3o8zjQ4RBw2fxSAMBfT1AeLUFYlp5jbGiLywfUXaTdfh0uYO5lbJs3nBFpAwlagiBitO1lHcKZxUDpkoQ/trelHwAmpxt8FP44sPEdYGJMzSqn5fL5ZQCAv9KABYKwLqcku0HdRYB7mhtgpXALAz8GkTaQoCUIIsZpyW4w+2I24StB9jYzQbuqpnD6NxUvYIMWIkGgZZeaVU7L5QvKATA/b3CCPHQEYUlOb2evc9drv+950j7bPwBGerXfP2FZSNASBBGjcQd7rUvcbgDEKrQzClpBiMV38Quaxiwuz0VZjg/jExHsaqKLGUFYjmgEaNnNtusu1H7/ORVA+VIAIlC/Tfv9E5aFBC1BEIyJ0diFJsHpYAAwOBrCqR42zGDlTIIWAGZfxl5545nGCIKAy7jtgHy0BGE9ug4BoSHAmwuUnqPPMch2kJaQoCUIgtG8k+VC5lYDBXMS/tg+qTpbV5SFomzvzG/mQrn9A2BsQOlKZ+TyBSRoCcKy8KbTmvPZyFo9mPtx9tqwQ7dx24T1IEFLEASj4S32OvsSZg9IkITsBpycCqB4IQARaHxLwSLPDm8M29nYi7FQWJdjEAShEC5oa9fpd4zq1WwwzHAX0F+v33EIS0GCliAIRuse9prkhSahhrB4ZktV2tP62A7ml+agMi8DoXAUOxvIR0sQRhGNivjz4TZ869l9ePbDFkSiHxlwIorGCFqXj4laAGh8W7/jEJaCBC1BEEA0DLTvY9t8AEKCyBXamSK74uGNYQ3bkzpOosT7aN+q79blGARBTObPh9sw/94XcPVPt+OBbUfxt4/swPx/fRHPfdgSe9PAaVY1dXqAqlX6LmiW1HDW9I6+xyEsAwlagiCA7iOsKcyby+K1EsQ/FmsIS7hCW3cRiwTrOwUMdShZ7Vm5SBq/+1Z9jy77Jwgixv7WfnzqF2/hdO8w8jM8+NyqWSjM9KChbxg3/Ort2OS+5vfYa+VKVkXVk3hBSz7atIAELUEQMbtB1aqk8meTagjj+PKAMqm7mV/gNObiuWxi2HsNvZiIRM/yboIglNI7PI5NP9+BsYkINiyuQNsPPonf3HIRWr7/SWxaVo1QOIpP/2IH+oaDQNO77EO1H9N/YTXnAw43EGgDBhv1Px5hOiRoCYIAWt9nr9VrkvrYh22DAIAVVQXJHa9GuqDpNGBhSXkeCjI9GAmFsb9VnzQFgiCALzzxLpr6RzC3OBu/veVCZHpcAIBMjwuPfWEd5pXkoHlgFJt//S5E/vtee4H+C3NnAlUr2XYj2Q7SARK0BEHEKrRJCtqD7YMAgGVKBS1vENEYh0PAhXOY7eBt8tEShC7sONWFrUc64HE58PxXLkVB5uSnNHkZHvzxyxfD63Lg7SP1EPpOsj/gDVt6I9sOqDEsHTBE0D700EOoq6uDz+fD2rVrsXv37mnfe9lll0EQhDO+rrnmGvk9X/rSl874840bNxrxoxBE6jHWD/ALTVVyFxouaJdW5id3TP7IsfMgEBpO7rMJQj5agtCX+/58CADwdx+bi3Mq8qd8z7KqAnxp7RysdDWzb+TXApkJNpCqhQtaqtCmBboL2meeeQZ33XUX7rnnHuzbtw/Lly/Hhg0b0N09ddXkT3/6Ezo6OuSvQ4cOwel04jOf+cyk923cuHHS+37729/q/aMQRGrSJqUbFM4FMhNs7AIQiUZxSKmgzatmAxzECNC2N7nPJgj30b5d3w2RmkIIQlPea+jFX453wuUQ8N0rl8z43m9+fDHOdzcBAPwF5xqxPEbNWtYT4G/WrQGVsA66C9oHH3wQt912G2655RYsWbIEDz/8MDIzM/Hoo49O+f7CwkKUl5fLX6+//joyMzPPELRer3fS+woKknzkSRAEQ6Hd4HTvMMYmIvC5nZhXkp38cWvXstdmfXy0q2oK4XU50DMcxInuIV2OQRDpyn1bDwIANq+dg1mFM//+zy/NxfXF7EnJa/5y3dcm480BSiWxzfsEiJRFV0EbCoWwd+9erF+/PnZAhwPr16/Hzp2Jeed++ctf4sYbb0RWVtak72/fvh2lpaVYuHAh7rjjDvT19U27j2AwiEAgMOmLIAgJWdAmZzc4IDWEnVOeB6dDwalEbgzTx0frdTuxtq4YAOXREoSWnOgK4JXD7XAIArZceU5Cn1npZJaDXzTmon1wVM/lTYbbqPh5jkhZdBW0vb29iEQiKCsrm/T9srIydHZ2nvXzu3fvxqFDh/DlL3950vc3btyIJ554Atu2bcMPf/hDvPnmm7jqqqsQiUSm3M/999+PvLw8+aumpkb5D0UQqYQoAh0fsu3KlUl9NNYQlq/s2NxH27IHiE79u6uWi6TGsHfIR0sQmvHU+40AgKuWVGBeSc7ZPzDaD98wG7CwK1SN33/QrOPqPgJ/8tRGFdpUx9IpB7/85S+xdOlSnH/+5MlFN954I6677josXboUmzZtwksvvYQ9e/Zg+/btU+5ny5Yt8Pv98ldLS8uU7yOItGOoAxjtBQRnLBs2QRQ3hHFKzwE8OUBoiA120IF1s1mFdlcTjcAlCC0QRRG/kQTt51fXJfahjg8AAAMZNfCLmfjDfjME7T4gMmHccQnD0VXQFhcXw+l0oqura9L3u7q6UF4+s49mZGQETz/9NG699dazHmfOnDkoLi7GqVOnpvxzr9eL3NzcSV8EQQDoPMBeSxYmPbnngFpB63DGciJ1qp5wy8HRzgAGR0O6HIMg0ok9TX041TOETI8T1y2rTuxDUuOpt4Y9/n/ndI9xtoPi+WyYS3gM6D5szDEJU9BV0Ho8HqxatQrbtm2TvxeNRrFt2zasW7duxs/+/ve/RzAYxM0333zW47S2tqKvrw8VFRWq10wQaUWHJGjLlyX1sZFgGPW9rNEq6QzaePg8d560oDElOT7MLWYNK7ubpvfZEwSRGLw6e/3SamR73Yl9qGM/ACCzbjU+VlcMUQSe/dCgJ6WCg3y0aYLuloO77roLjzzyCB5//HEcPXoUd9xxB0ZGRnDLLbcAADZv3owtW7ac8blf/vKX2LRpE4qKJufVDQ8P41vf+hbee+89NDY2Ytu2bbj++usxb948bNiwQe8fhyBSi07JP5ukoD3S6YcoAqU5PpTmqJjJzn277R8o38dZ+JhkO3ivkWwHBKGGSDSKp/ey+K2b1sxO/IP897vyPHz6vFoAwB/2G2j947YDSjpIaVx6H+CGG25AT08P7r77bnR2dmLFihXYunWr3CjW3NwMx0c6pI8fP463334br7322hn7czqdOHDgAB5//HEMDg6isrISV155Je677z54vQnOkicIgsEtB+VLk/rYgTY2Tlax3YDDLQfdR4CJUTauUmM+VleMp/Y04r0GErQEoYbtJ7vRNTSOoiwvrlyc4BPRkR4g0AZAAMqX4VPZAv7p2X3Ycaob3UPj6m6IE0UWtFShTWV0F7QAcOedd+LOO++c8s+mauRauHDhtEHoGRkZePXVV7VcHkGkJ2MDwKDUnJGkoD3U4QeggaDNqQSyy4HhTmZ/4MkHGvKxuliFVhRFCIKg+TEIIh145XAbAOC6pVVwOxN8wNvFpomhcA7gzUGdF1hdW4j3m/vx/IFW3HbhPJ1WGwe/ce6vB0b7jJtURhiKpVMOCILQkU4WjI78WiAjOR/s0U4maJeU56lbgyDELjbt+vhol1Xlw+d2YmA0hJM0YIEgFPPqUTZta+OSysQ/1CU1YsWlqFx7bhUAYNvxs8d3akJGIVAkCWcd7U2EuZCgJYh0pVNZQxgQE7SLyjRIDOE+Wp1G4HpcTqyqYSN9yUdLEMpoGRjB4Q4/HIKA9QuTmPbFI/nKYiNvL1/ALIfbT3YZN5a6YgV7lRrUiNSDBC1BpCu8Qlu+PKmPDQcn0DzAIncWl2sgaHWu0AKTbQcEQSTPa1J1ds2sQhRmJdGvwi0HcRXatbOK4XM70TU0jqOdBk3urJDOcyRoUxYStASRrvAKbUVyFdrjXewCVJztRXG2Bg0dleex1/7TwFi/+v1NgZx0QI1hBKEI2W6wOAm7QTQMdB9j23GC1ut24kJpit9fTxhkO+AV2vYPjTkeYTgkaAkiHQkHgZ7jbDvJhrBjkqBdrIXdAGD+tgIpAkgnfxuv0B5oH8RIMKzLMQgiVQlHonj9GBOeGxJNNwCAvnogEgQ82UD+rEl/dPl8Zjt440TXVJ/UHl6h9TcDo/rcOBPmQoKWINKRvpOAGGETdHKSqLgg5p9drLYhLJ4qffNoqwsyUZWfgUhUxN4WGrBAEMmwp6kPg2Mh5Gd4sGZWEgkB3G5QupgNOIjjb+J8tNGoAT5aXx5LWgDkUbxEakGCliDSke6j7LVkMUsaSALuedNU0HIfL7dB6IDsoyXbAUEkxWvHmN3gikXlcCUa1wXEEg5Kzznjj1bPKkKWx4X+0RAOSmO0dUe2Hew35niEoZCgJYh0hAva0sVJf/Rol1Sh1cpyAMR8vB0GCNpGqtASRDK8Vd8DIJZOkDDdPLLr3DP+yO104OJ5ko/2pFG2gxXslRrDUhIStASRjvTEVWiTYCISlbNcdanQDjQA437t9hsHbwzb2dBjXFQQQdiccCQqP9W4SGrkSpgpMmjj4T7aN40StJUr2CsJ2pSEBC1BpCMKK7T1PUMIR0VkepyoztdwTG1mIZBbzba5705jVtYUwuUQ0BkYR4sUO0YQxMwcaB/ESCiMvAw3zqnIT/yD437A38K2y5ZM+RZ+k7mn2aCnJrwxbJAaw1IRErQEkW5MjAIDjWw7SUF7VEo4WFSWB4dD4xGyOtsOMj0uLK9iE9Eoj5YgEuPt+m4AwAWzS5L7necDFXKrAV/+lG9ZWVMIhyCgbXAM7YMG3GT68mOJKlSlTTlI0BJEutFzHIDI5plnJfcIMZZwoKF/lsMnlunZGEZ5tASRFO+cZv7ZC5O1GyTwFCjb68YS6VxiWJWW2w50PM8Q5kCCliDSjR4p6Lx06seAMyEL2jIN/bMcIwQtTQwjiIQRRRFv1ysUtL0n2GvJwhnfxmPA9jQZJGh5g5pO1ibCPEjQEkS6wR8FlixK+qPHpYawRVomHHC45aDnGBv8oAO8Qru3pR+hcESXYxBEqtDUP4J2/xhcDgHn1yWRPwsAvdLgluKZBe35sqA1yNNaJg2S6SRBm2qQoCWIdEOu0CbnnxVFUU44WFCao/WqmNcuo0Aal3lE+/0DmFucjfwMD0LhKA516JOmQBCpArcbrKwpRKbHldyHe5Ks0Db3GZM+Ui5VaHtPAOFx/Y9HGAYJWoJIN/hs9SQju/pGghgcCwEA5pboIGgFQXfbgSAIWF1bCAB436hHnARhUxT7Z4NDQKCVbRcvmPGtSyvz4XE5MDAaQn3vsJJlJkdOJRu3LUZiPl8iJSBBSxDpRHCIzTIHgNLkLAcnpOpsTUFm8tWaRCnXf8BCfEWIIIjp4c2TFyj1z2aXsacuM+BxOXFeNXuPIT5aQYhVaclHm1KQoCWIdEK+0JSzKkUScLvBfD2qsxwuaHW80MgV2mbKoSSI6RifiMi2nDWzkjtXyOeZs1RnOWtq2U3mbsMaw8hHm4qQoCWIdEK+0MxP+qMne1gGra6Clk8U6j4C6OSnWy1dPA+1D2IsFNblGARhdw62D2IiEkVRlhe1BVnJfZj79M/SEMbhDWe7mwxKH6Gkg5SEBC1BpBO9J9lrUfKC9oTcEKZDwgGneD7gcAHBQGzKkMbUFGSiNMeHcFTEh22DuhyDIOzOXsmSs6qmEIKQ5BCVBCO7OKtqmKD9sHUQ0aiBjWFdB3W7cSaMhwQtQaQTfck9CoznZI9kOdAj4YDj9MSqOnwOvMZMagwjHy1BTMneFmbJWVWbpN0AkIa3IGFBO780Bx6XAyOhMBr7DWgMK17IbpzH/bHmNcL2kKAliHSi9xR7TdJyMDmyS8cKLRBnO9BH0AIxzx75aAliavZKvxurkxW04XFgoIFtJ2g5cDsd8rCWg+2DyR1PCS5vLIebfLQpAwlagkgXohGgv55tF81L6qMd/jGMhMJwCAJmFyXpp0sWPsFMpwotELtIGzadiCBsRDCuISzpCm1fPSBGAW8eSzlIkGVV+QAMErRAzEfbedCY4xG6Q4KWINKFwSYgEgJcPiCvJqmPcrtBXVEWPC6nHquLwS80Og1XAIDVUnTX0S4/hoMTuh2HIOyIqoYwPiGsZAGLyEqQpZX5AIADRvnaDXgSRBgLCVqCSBf6JLtB4VzAkZwo5YJWlwlhH4VfaHpP6jbJpzw3A9X5mRBF4IOWAV2OQRB2RfbPKmoIkxpPE7QbcLigNaxCy58E8UQGwvaQoCWIdEGO7ErObgAAJ7p5ZJfO/lkAyKkAfPlskg9vLtEBsh0QxNRw/6yihjB+45ykrYkL2hPdQxifiCR/3GThHtq+U+zJFWF7SNASRLogR3YpSDgwYqgCRxCMsR3IjWEkaAkinvfjIruSRqGgrczLQGGmB1FRxJFOf/LHTZbcKsCbC0TDsTUTtoYELUGkC338UaCSoQoGWg6AuMYw/TqQ+QhcSjogiBgTkajcELYyWUEriqwpDEha0AqCELMdGOGjFYRYrFj3Uf2PR+gOCVqCSBd6lQnaaFREfS/LhpxnRIUWiPlodUw64NWnkz1DGBylR44EAQDHuwKYiESR63OjLtlEk9E+IOgHIACFs5M+tuE+2pLF7JV8tCkBCVqCSAfGB4GRbradbGRXYAzjExE4HQJmFeoc2cWJH4GrE0XZXswuygYQa4IhiHSHi8lzK/OSbwjjj+7zaliaSpIsqyoAABxoN6hRs5QLWqrQpgIkaAkiHeADFbLLmW8sCep7md1gVmEWXE6DThm8YWO4CxjT7+K2ZhZNDCOIeA60sd+3ZZUFyX9Y9s/OVXRs4yu00nmGLAcpAQlagkgHVPhnT0t2g7nF2VquaGa8OUBuNdvWNemA+Wgp6YAgGFxMcnGZFHxwS6EyQXtuJZsW1hkYR++wPpF9k+AV2v7TukUEEsZBgpYg0oE+5Rca7p+dU2SgoAViDRs6+tt4dBc1hhEEQ5WgVZhwwMn2ulFTkAmAeXl1J7tcigiMxnoMCNtCgpYg0oGB0+xVwaNAuUJrVEMYhz8O1FHQrqphFdqm/hH0DFGFhkhvBkdDaB4YBRCrliaFwoSDeBaVMUvUMSMErSDEqrRkO7A9JGgJIh3olwRt4ZykP8o9tMZXaPUXtLkZbiyULqDkoyXSnUMdgwCAmoJMFGR6k/uwGI2dZ4qSP89wFpay38fj3QYIWiDuPEOC1u6QoCWIVEcUgT7lgjZWoTVL0OrnoQXIdkAQHFV2g0A7EB4DHC4gf5biNcgV2k6jBC1Fd6UKJGgJItUZ65eyIQEU1CX10aHxCfQMBwEAc4qMthxIE82G2oFx/SYH8Txaiu4i0p0D0kADVf7ZgtlM1CpkUTmzOhhWoZUtB/pFBBLGQIKWIFId/hgwtwpwZyb1UV6dLc72IjfDrfXKZsaXD+RUsm0dq7R8Xv1eqtASaQ6v0C5Tk3CgMLKLwy0H9b3DCIUjqvaVEFzQDjQBE6P6H4/QDRK0BJHq2NE/yzEg6eC86kIIAtA6OIpuagwj0hRRFGUPrbIKLU9SUd4QBgBV+RnI8rgQiYryDbWuZJUAmUUARN3tTYS+kKAliFSHC9qC5EdRmpJBG48BPtocnxsLpKrQXmoMI9KUloFR+Mcm4HIIcqNkUsgVWuUNYQAgCLHjG5J0AAClS9gr+WhtDQlagkh1VFVopQxa0wWtvh3IvDGMfLREunKkk/nUF5TmwuNyJr8DFeeZj8IbwwxPOiAfra0hQUsQqY48vUdFwkGxwQ1hHIOSDnhjGCUdEOnKUUnQLi5XUJ0Vo8yDCih6EvRRjE860D8ikNAf5a2IhK0ZC4Xxb68dxuEOP0ZDYSwqy8V91y5Hjs/gxh9Cf7Tw0JpWoZU8tIFWIBgAvAoutglAjWFEunNUEo+LyxUMVBjqACJBlm6QV616LQuNrtDKSQckaO0MCdo0ZGA0iOt+9iberu+Rv/fq0Q5sO96JF75yGWabJV4I7RnrB8YG2HaSgjYciaKpfwSAiRXajAI2nnK4k42mrFqly2E+2hhWmuPT5TgEYVWOdkkVWkX+2Qb2mlejKrKLEz8tTBRFCIKgep8zwrNo/c1AcAjwmnS+I1RBloM0o3toHBf/5+t4u74HeRluPPi3K/HTG9agPNeHQx1+rPmPrTjVM2T2Mgmt4Bea7HLAk5XUR1sGRhGOivC4HKjMy9BhcQnCq7Q6jqakxjAi3VFVoR2QzjOF6u0GADC/JAeCAAyMhtAr5WDrSmYhkF3GtinpwLaQoE0zvvXsPhzu8KMyLwNvff0KfONvFuOrFy/Anm9dhRXVBegbCeLO3+2BKIpmL5XQAi5oFdgNGvuZf7auMAsOh84VkpkwyN9GAxaIdKVnaBx9I0EIAhQmHEjnGQ38swCQ4XFhViG7ATcs6YBG4NoeErRpxJ6mPjyxm514nr3tEiytKpD/rLogE7/7u4vgcTnw6tEOPH+g1axlElqiwj/b2MfsBnWFJltQ+IWm15jGMPLREukGtxvMKsxCpkeBZWBAW0ELsCotAOOeGHLbgY5Pggh9IUGbJoiiiK//8X0AwObzZ+P8uuIz3jO/NBff/Bv2S/31P+7FWChs6BoJHRjgGbR1SX+0oY9VaGcXJWdV0Bw5UkfnCi1FdxFpimw3KFNgNwA0txwAwDxJ0PLGVN3hjWFUobUtJGjThN/ta8K7p3uR5XHh/utWTPu+/7vhXFTnZ6KpfwQ/fpO8RLZnoJG9KrjQNEoNYXVmTQnjcEHrbwZC+k0O4o1hLQM0MYxIL1RFdgGaWw6AWCPqqR4DpoUBlHSQApCgTRMefIP9kn77iiWozM+c9n1ZXhf+9ZplAICfvXMK0Sh5aW0NF7QKLjSNfTEPralkFgJZpWy794Ruh8nNoMYwIj1R1RA2NgCMD7JtBU+CpoNPJzSsQstvnIfagXG/McckNIUEbRpwoG0Au5v64HY6cMdF88/6/htWzkKuz43TvcN440SnASskdGFijOVDAoouNJap0AKxpAODBiyQ7YBIJ1RFdvGb5uyypJNUZiJmOTCoQuvLY2kwANB3yphjEppCgjYN+OVONinquqVVKEkgXzPL68JNa+oAAI+8S7/YtsXfwl49OUBGYVIfDYUjaBscA2ABDy0Q56PV199GjWFEujEcnEDLwCgAYJESD60ODWFAbJjLwGgI/SMGRHcBQLFU8NHxSRChHyRoU5zxiQielJINvnzBvIQ/d5v03mc/bEUP+QntiWw3mAUkGUzeMjCKqCjC53ZaY8iAUUkH1BhGpBl8vGxJthdF2d7kd9CvfUMYAGR6XKjIZfnXhlVpixewV8qitSUkaFOc5z5sQf9oCDUFmbhiUXnCnzuvphCrawsxEYnKUV+EzZAFbV3SH5XtBoVZ+k/pSQSDsmjPq2aCtmVglG7kiLRAthso8c8CulVoAWBuieSjNSq6iwvavpPGHI/QFBK0Kc6v3mOxTV9aOwdOR3J/3byi++QeErS2ZKCJvebXJf1RuSHMCnYDICZoB5qAiVHdDsMaw5h3j6q0RDpwopuJxYWlChMOVNw4n415POnAqMYwshzYGhK0KYx/LCQ3dX3h/OTvnj+5vAaCAOxvHUDboH4igtCJwUb2qqJCO9sKDWEAkFUMZBYBEHW/2KyuLQJAPloiPTgpCVp+I5c0OkR2cWIVWqMsB1Lzaf9pIDJhzDEJzSBBm8K8drQD4aiIhWW5mK/g7rs0x4e1s9gAhpcPtWm9PEJv4j20SdJglciueGTbgTFJB+9TdBeRBpzoZh7aBUoqtJEQEJCuDTpUaHkWrWHRXbmVgDsLiIZj50/CNpCgTWFekkTotedUKd7HtedWTdoXYRNEUZ2Hts9CkV2cIv44UF9/GzWGEemCKIqy5WC+kgqtvwWACLgygKwSbReHWBatYcMVBAdQLDVP69yASmgPCdoUJRKN4s9H2gHERKkS+Gf/cryTRuHaibH+2FSt/NqkP97Yb8EKrUENG9QYRqQLnYFxjITCcAgC5ii5eR1sZq/5tUknqSQCz6LtCIxh1KjrDz/PkI/WdpCgTVH2NPWjZziIvAw3Lpyr/M55WVU+qvMzMTYRwV9Pdmm4QkJXeHU2pxJwJRe7FZyIoN3PM2gtVKGVLzT6ClpqDCPSBW43qCvKgtftTH4HvPFUga0pEQqzvMjP8AAAThsV3VVkzHmG0B4StCnKS4daAQAbF1fC7VT+1ywIAtkO7IgcpZP8haZlcBSiCGR6nChWkkupF/xRYN8pIBrR9VA0YIFIB072qGwIG+RJKvoIWiDedmDUCFyq0NoVQwTtQw89hLq6Ovh8Pqxduxa7d++e9r2PPfYYBEGY9OXzTa4wiaKIu+++GxUVFcjIyMD69etx8iTdTcXz0iH1dgNOvKAVRVH1/ggDkCsndUl/tKGX2w2yrZFBy8mrBZxeIBIE/M26HmoVTzqgCi2RwvAK7fwShZFd8ZYDnTB8BG78kyC63tkK3QXtM888g7vuugv33HMP9u3bh+XLl2PDhg3o7u6e9jO5ubno6OiQv5qamib9+Y9+9CP8z//8Dx5++GHs2rULWVlZ2LBhA8bHye8GAF2BMXzYNgBBADYuqVC9v8sXlMHlENAyMCo3CxEWh1sOFGTQNg3whjAL+WcBwOEEinjDhr4jmWMVWko6IFKXE2ojuwyo0PIRuDwbW3cK57DmsKAfGJlepxDWQ3dB++CDD+K2227DLbfcgiVLluDhhx9GZmYmHn300Wk/IwgCysvL5a+ysjL5z0RRxH/913/he9/7Hq6//nosW7YMTzzxBNrb2/Hcc8/p/ePYgrfqewAASyvzUZytfmxppsclZ3O+VU+/4LZARQZtk5RBO8tKDWEcuQNZ38eBKyVB20yNYUQKE8ugVTpUQV8PLRBrTG0wStC6fDGBTiNwbYWugjYUCmHv3r1Yv3597IAOB9avX4+dO3dO+7nh4WHMmjULNTU1uP7663H48GH5zxoaGtDZ2Tlpn3l5eVi7du20+wwGgwgEApO+Upkdp5jovGRuqWb7vHheyaR9ExZHnhKW/KPAZknQ1hZYUNAWGeNvo8YwItWJRKPyBK75JQoqtBOjsQqmjpYDHh3Ih70YAiUd2BJdBW1vby8ikcikCisAlJWVobOzc8rPLFy4EI8++iief/55PPnkk4hGo7jgggvQ2sqanPjnktnn/fffj7y8PPmrpqZG7Y9maWRBO09DQSuJY6rQ2oBoJBZ2ruBC0yQL2kwtV6UNcnSXvpYDgBrDiNSmZWAUoXAUHpcDtYUKftcHW9irNxfwFWi7uDh4hbaxb8S4Hg6DElUIbbFcysG6deuwefNmrFixApdeein+9Kc/oaSkBD/72c8U73PLli3w+/3yV0tLi4YrthaDoyEcaB8AAFysoaC9aG4JBIF5rroCY5rtl9CB4U426cbhAnKS91A3D7Axx9a0HBg3a50aw4hUhvtn5xXnwOlQIAXi/bM6No/WSuehkVAYfSNB3Y4zCfnGmSq0dkJXQVtcXAyn04mursn5pV1dXSgvL09oH263G+eddx5OnWIVGf65ZPbp9XqRm5s76StVeed0D0SRPUIqz83QbL8FmV6cW5EPAHhb8ugSFoVXTnIrWSNVEkSiUbQOMkFba0VBy5vCRrqBsQFdD0WNYUQqIyccKG0IU2FrSgaf24kK6VpmWFMyVWhtia6C1uPxYNWqVdi2bZv8vWg0im3btmHdunUJ7SMSieDgwYOoqGCVptmzZ6O8vHzSPgOBAHbt2pXwPlMZPewGnIulAQ07yHZgbfySoM1L/kLTGRjHRCQKp0OQLyKWwpvDhkUAutsOzqthj1GbB0bRO0yNYURqwTNoFflngVjjqY4JBxyeuGKYj5YLWn9LbOIiYXl0txzcddddeOSRR/D444/j6NGjuOOOOzAyMoJbbrkFALB582Zs2bJFfv+9996L1157DadPn8a+fftw8803o6mpCV/+8pcBsASEr3/96/j+97+PF154AQcPHsTmzZtRWVmJTZs26f3jWB49BS3f51vUGGZt5GzI5L3ivCGsOj8TLhUDOXRFth3oWz3Jy/DIF3vy0RKpxinVglY6z+iYcMCJ+WgNEpeZhUAmsxwZ4dcntMGl9wFuuOEG9PT04O6770ZnZydWrFiBrVu3yk1dzc3NcMT5dwYGBnDbbbehs7MTBQUFWLVqFd59910sWbJEfs+3v/1tjIyM4Pbbb8fg4CAuuugibN269YwBDOnGaCiM96XHoxdrmHDA4fv8sG0Q/rEQ8qSRhITFkCu0CgTtgIUbwjhF84GGNw3x0a6uLcTJniG839yPDUsqdT8eQRgFH1QwV7GgNcZyAJiVdLAQaH6X3ThXrDDuuIRidBe0AHDnnXfizjvvnPLPtm/fPum///M//xP/+Z//OeP+BEHAvffei3vvvVerJaYEuxv7EI6KqMrP0CUUvzI/E3VFWWjsG8He5n78zcLEfNCEwagQtHLCgRX9sxy5YUN/f9uq2iL8dm8TNYYRKUUkGpVzXflo2aQZ0H+oAsfwCi3AngQ1v0vRXTbCos8UCSXskaqzH6sr1m1kKR+w8D41ylgX3hSmxHLAEw6smEHLMchyAFBjGJGatA2OIRSOwu10oEbJ05hxPzA+yLZTtkJLWbR2gwRtCrGniV10uejUg9W17AL/PnkKrYkopkGFVhK0/aeByISuh6LGMCIVqZcGKtQVZimL7OLnmIwC1qipM5RFSyQCCdoUgovMNbP0FLSUzWlpxvrZBB8AyKtO+uPcQ2vJDFpObhXgzgSiEzEfn05QYxiRisT8swrtBvJTIP2rs4DZWbSn2LAawvKQoE0R+oaDsieKPybVg5VSxep07zD6jTqxEInDO4+zStlM8iRp7pcyaK1sORAcsTxaQwYsSLYDuokjUoR6KeFgbrHC6qqKp0BKMCWLNq+GnUMjQd1vnAltIEGbInBP6/ySHORn6pc+UJDpxTyqWFkXv3L/bGBsAoNjIQBQNgrTSAx8HEgjcIlU41SvyoYwfuNskKAFTMiidTjjbpzJdmAHSNCmCNxuwD2uesIv8NQYZkEG1Ud2FWZ6kO11a7kq7SkycgQu/XsnUgu7VWiBmI+2weikAwDoPW7cMQnFkKBNEXjCgZ7+Wc5qegRrXbTIoLWyf5bDLzQGRHetlG7gqDGMSAVEUVTvoVXxJEgps3nSgZGCtsi4RBVCPSRoUwRePdIz4YATi+4iQWs5/MqbNeSEAyv7ZzkGWg6oMYxIJfpGggiMs3SQOUUqm8IMrNDyRlVTortoWpgtIEGbAnT4x9A2OAaHIMgxQ3rCK1ZN/SPoGaKKlaVQU6Htt0HCAadoLnsd6wdG9bcCUGMYkSrw6mxlXgYyPApmK4XHgRFp/LlBKQdA7LzUImVlGwL30BrwJIhQDwnaFIBXZxeX5xrifczNcGNBqVSxogu8tVAxVKFlkF0oqvMztFyRPrgzgTzpYmqEj5Yaw4gUgftn5ykdeetvZa/uTCBD/54NTo305IhbowyBC9qRHmBswLjjEoogQZsC7JNEpZ5xXR+FV2k/bKNfcssQGmEVS0BRhbZVqnzU2MFyAMQ1bFB0F0EkSr2WCQc6TaScCj7RzD82gcCYvgNVZLw5QE4F2ybbgeUhQZsCHGgbBACsqNbfbsBZWpkPADjYPmjYMYmzwO0G3jzAl5f0x3mFVtEoTDMwsGEj3mZDjWGEnYkJWpUJBwY2hAFAjs+NAimSsmXQSB8tNYbZBRK0KcABSVQuqyJBm9aosBuIoohW2XJgE0FrYNJBXoaH8peJlICPvVVeoTW+IYzDb7abjWwMKzLuPEOogwStzRkOTsgnqGWSyDQCLmiPdgYwEYkadlxiBvzKw857h4MIhaMQBNYsYgsMrpxQXB2RCpyWKrRzlApaEzJoObWyj9bIxjDjrE2EOkjQ2pzDHX6IIlCe60NJTvKjTpUyqzALOT4XJiJRnOgOGHZcYgZUVE54dbYsxwePy6nlqvSDX2gGGoFISPfDUWMYYXfGJyJo948BiOW6Jo2KaEC11EoV2hYjG8PkJ0HkobU6JGhtDvfPLqs0zm4AAIIg4NyKfADAQWkNhMmo8LbxKBzb2A0A1qzhyQbECNDfoPvhqDGMsDs8azrL40JxtlfZTky1HJhQoeWCtv80EI0Yd1wiaUjQ2pwDUsrAsqp8w49NPlqLoeJRoO38swDrsDYwJzK+MaxvOKj78QhCa/jY2DnF2RCUJBREw0CgjW0b3BQGxFdoDRS0eTWAy8eeAg02GXdcImlI0NqcWENYvuHHJkFrMQaVPwq0paAFDPXRTmoMoyotYUO4oFVsNxjqYE9EHC4gu1zDlSUGH8ttaBat4AAKpUEulHRgaUjQ2hhRFE2zHABxgrZj0PBjEx8hEmIXG0BRhZZ70mwT2cUpMtbfFvPR6j+djCC0hjeEzS5SmDXNhyrkVgEO4732NXEV2mhUNO7ABiaqEMohQWtjWgZGMTgWgsshYFFZruHH54K2sW8EQ+MGBV0TUxNoAyCyR2NZJUl/vHWQNYrYrkJr8GhK7qN9nxrDCBuiukJron8WACrzMuEQBExEoug2Mg/awMxrQjkkaG0M988uKsuF12383XJhlleOeDpEtgNzkS801Yqm98iWA7tVaA2O7pIrtGQ5IGxIQx97EqNY0AakCm1etUYrSg630yFfcwzNoqWkA1tAgtbGmDFQ4aOQj9YiqGgIix+qUJNvk7G3HO5tG+sHRvW3AVBjGGFn4pvCFOGXGsJyqzRaUfLUFkrDFQzNopWeBFEWraUhQWtjuIhcauBAhY9CgtYiqHgU2DcSxPgEi6OxzVAFjicr9jMbUKXNz6TGMMKe+MdCGBhlec11Sj20POEgzzxBy2+6TcmiHekGxv3GHZdIChK0NuZIJ/vFOrciz7Q1LClnxz7WRcMVTEVF2Dmvzpbm+EyxrqjGaB8tNYYRNoRXZ0uyvcj2upXthFsOcs2xHAAmVWi9ubFUB7IdWBYStDYlEo3iuCQiF5ebJ2h5MxoJWpNRMfa21Y5DFeIxuGGDBiwQdiSWcKDQbgDELAcmVmjl8bdGemiBOL8+2Q6sCglam9LQN4JgOAqf26n88ZEGLJQEbevgKCUdmMmgiilh3D9rt4YwjsENG9QYRtgR1Q1hE6PMqw6Y6qGVo7sGDazQAnE+Wko6sCokaG3KUclusLA0F06HeX+NhVlelOb4AAAnuqlKawpiNM7blo4VWmMtB7wxrLGPGsMI+xCL7FLqn21nr+4swJevzaIUYF6FdgF7pSxay0KC1qZwQbu43Pj82Y+ymGwH5jLcxQYrCA4gtzLpj9t2ShhHnrXeAET0f0qQn+nBXKlLnKq0hF2IJRzkKNsBH6qQV6UoGlArqqTzVPfwOELhiHEHNniIC5E8JGhtypFOJh6XmOif5ZCP1mS43SCnko2kTJJWu1sOcqsAVwYQnTBs1vrq2iIA1BhG2Af1FVrzI7sAoDjLC4/LAVEEOgJjBh6YPwmqB6IGCmkiYUjQ2pRYhdYCgpaSDsxFZdh5i90tB4IjdrExuDGMJoYRdkAURTSq9dBaIIMWABwOQY4XbBs0UNDm1QJOLxAJxppwCUtBgtaGiKKIo13WsRzwCi0X2YTBcG+bgs7j+KEKthW0QNzjQGME7RqpQru7iSq0hPXpHhrH2EQEgqDiSYzJU8LiqcpjP0ObkY1hDidQJA1y6SXbgRUhQWtD2gbHMDQehtMhYH6JQj+UhnBBe7JnCOFI1OTVpCG8cpKTvH92YDSEMWmoQlUqCFqDKrSrZxXCIQhoHRxFu9Hd1gSRJE1SA1VlXgY8LoVZ0xaxHABAVb5UofUbWKEFDG9AJZKDBK0N4ZXQeSU5yk9OGlJbkAWf24lQOIpGoztPCWCIJxwkXznhdoOSbC98dhyqwDE4uivb68YS6ekIVWkJq8PPy3WF9s6g5ZhSoQXibpwpi9aKkKC1IbLdoMx8uwHAPE0LS6XGMLIdGI8Kb1tK2A0AUyona+uKAZCgJawPr9DOKlSRWS5XaC1gOcg3SdDK0V1kObAiJGhtyJEOJhqtkHDAoaQDE+Ee2nSM7OJwQTvSA4wNGHJILmh3NfYacjyCUIpqQTvuB4LSud0SFVpmOWg1XNAaa20ikoMErQ05aoGRtx+FN6eRoDWYaBgY7mTbCiwHto/s4nhzYh5ig6on589ijWF7mvsQjYqGHJMglKBa0PLqrC8P8KiwLWhEtXS+Ms1DO9wZE/iEZSBBa0OOyYLWGpYDIC7poIssB4Yy1MEmhTncQFZJ0h+3fWRXPAZHd51TkYdMjxND42G6kSMsjWpB67eO3QCY7KEVRQNvJn15QFYp26YqreUgQWszBkdD6B4aBwAsKLWOoJ0vreVUz7DJK0kzZLtBBctjTRLZcmD3Ci1geHSXy+nAKmkM7u4msh0Q1kV9hTZuSpgF4Dm0wXAU/SMhYw9OPlrLQoLWZpzsGQIAlOf6kONzm7yaGPOkcYrdQ+MIjOk/fpSQ4OMoFUbpyJaDfBXNIlbBYEELxPtoqTGMsCaDoyEExtk5ubZAbYXWGoLW63aiONsLAGjzG510YOyTICJxSNDajBPd7NGmlaqzAJCb4UZpjg8AcKp3yOTVpBFyhVbZUIWWAVa5SYkKrdywYVzlhPtoqTGMsCqN/eypWUm2F1ne5EdjA7BUBi3HtOguOSKQorusBglam3Gym4nFBaXmD1T4KHzIw8lu8hMahnyhST7hYHAshNGQNFRBeoRna3jlpP+0YbPWeYX2QPsgxkJhQ45JEMmgTWSXdaaEcfhwhVYjx98CcVm0ZDmwGiRobYZVK7QAG/QAkI/WUFRkQ7ZKDWFFWV5keBRWbqxEXo3hs9ZrCjJRluNDJCpiX0u/IcckiGTQRNBazHIAWKBC21/PGnIJy0CC1macsEGF9lQPWQ4MI6B8ek/KRHZxHM44f5sx1RNBEGjAAmFpVAtaUYw7z1inQhuL7jJY0ObPApweIDwO+FuMPTYxIyRobYQoijjRY+UKLcsnPEmC1jhUDFVIqcgujhzdZZy/bW0d99GSoCWsh2pBO9bPxBtgsQotsxy0GW05cDiBwjlsmxrDLAUJWhvRNTSOofEwHIKAOUXmh1t/lFh0FwlaQ4hMAEPSUAUlloNUmRIWjwlJB7wxjCq0hBVRn0Er+WezSgCXV6NVqUcef2t0hRagiWEWhQStjeD+2bqiLHjdTpNXcyY8uquLoruMYagDgCgNVShO+uMpZzkA4gStcQ0bayRB29A3jB4pI5ogrIJmU8IsVJ0FTKzQAqbcOBNnhwStjbCyfxaYHN1VT9Fd+hNvN1AwVCE1LQfGV07yMjzypDyq0hJWYiQYRs9wEIAGU8Is5J8FYhXavpGg8QkjctIBRXdZCRK0NsLKCQcc8tEaSECboQopJWhNmrUeG7BAebSEdWiWcqZzfW7kZ3iU7UTleUYvCjI98ElPKtv9Bldpi41/EkScHRK0NkKu0JZYs0ILAPNLyEdrGLxCqyDhQBTF1LQc+PKA7DK2beDFhny0hBWJtxsIgqBsJxa1HAiCIN+MmzYtbKgDCNK1ziqQoLURtqrQdtMvue7wR4E5ySccDI2HMRxkj+kq81JI0AKmBJ/zpIPdTX0QRdGw4xLETGiaQavgxllvTPPRZhSwJjmAqrQWggStTYhEo/LAggVlVq7QSlm05KHVnyHl3jZe0cjLcCsfh2lVioyP7lpamQ+f24mB0ZD8JIUgzEbTKWEKklT0Rk46MHq4AhB340yNYVaBBK1NaO4fxUQkCo/LYWnP4zx5/C1d1HVHxfQe7jmrSrXqLBDnbzPuQuNxObGmthAA8O7pHsOOSxAzoX6oQhQIdLBtK1ZopfG3bUZ7aIHYjTMlHVgGErQ2gacGzCnKhtNh3b82Lmi7hsYxHKToLl1RMVShXarQVkqP7FIKE6K7AOCCOewR5LsNJGgJa9DYz57qKRa0w91AdIKlqORUaLgybeA35K1mVGiLF7BXErSWwbrKiJhEfS87Mc0ttt5AhXjyMjwozGTdtA3SmgkdiISA4S62rcRyIHnOUlLQ8mlhfcbOWr9gNhO071CFlrAImmXQZpcDDutZk0y1HBQb79UnZoYErU3gqQHzLJxwwJkjie7TfSRodWOoE4DIZopnFiX9cdlyYGH7imLyZ7FhE+Gx2JQjA1g3m0V3He0MoH8kaNhxCWIqQuGI/Htep3ZKmAXtBkB8U5iJHtq+U4beOBPTQ4LWJsQqtPYRtA19IyavJIWRo3SUDVVIacuBwwUUzWXbBj4OLMnxyUNP3qM8WsJkWgdHIYqAz+2UB94kjUUjuzjVUuRgu38M0ajB6SIF5tw4E9NDgtYmcA/t3BJrWw4AYHaRVKEly4F+qGgIA1LccgDEJR0Y7KPltoN6sh0Q5sLtBrUFmcozaOXzjPUSDgCgPDcDggCEoyJ6hg0eO+1wAYVz2Db5aC0BCVobIIoi6ntsVKGVBS0lHeiGysoJr9CmpOUAMG00ZawxjCq0hLloGtllUcuB2+lAmVR9NiXpwIRR28T0kKC1AV1D4xgJheEQBOVeKAMhD60BqBC00agoe+tStkJrQnQXAFwoCdrdTb2YiJCvjjAPbQRtnLXJoshJBwNm+Gj5kyAStFaABK0NqJcawmoKMuGVZldbmXgPLU1N0gkVgrZ3JIhwVIQgsEd2KYlJ0V2LynKRn+HBaCiCA20Dhh6bIOJp7NNC0EoZtBb10AJxSQdGj78FKLrLYpCgtQF2iezi1BRkwSEIGJ+IoDNgsK8pXeCCVsGjQG43KM32we1M0VMAj+4KtAEh45oTHQ5BTjug+C7CTJoGNBiqMNzJti2YQcuRhyuYnXRAmI4hV7OHHnoIdXV18Pl8WLt2LXbv3j3tex955BFcfPHFKCgoQEFBAdavX3/G+7/0pS9BEIRJXxs3btT7xzANHtllB/8swHxNtYXsrpl8tDqhYqhCyjeEAUBGYSzOzOCLDbcdvHuafLSEeXDLQV2hwkLISA8QDQMQgOwy7RamMdxywM9rhjLpxpksdmaju6B95plncNddd+Gee+7Bvn37sHz5cmzYsAHd3d1Tvn/79u343Oc+h7/+9a/YuXMnampqcOWVV6KtrW3S+zZu3IiOjg7567e//a3eP4pp8ArtPBskHHDkxjDy0WpPJMQm+ACKuo9TOoM2HpMeB14wh1VoaWIYYRbRqIgWyVOqfKiCdNOcXQo43RqtTHuqzbQcmHjjTJyJ7oL2wQcfxG233YZbbrkFS5YswcMPP4zMzEw8+uijU77/qaeewt///d9jxYoVWLRoEX7xi18gGo1i27Ztk97n9XpRXl4ufxUUFOj9o5iGHNllkwotENcYRtFd2jPUATZUwatoqAJ/NJfSFVrAtOiu82cVw+kQ0DIwipYBymImjKcjMIaJSBROh6D893xI8s9a2G4AxFsOTKjQArEbZ5oYZjq6CtpQKIS9e/di/fr1sQM6HFi/fj127tyZ0D5GR0cxMTGBwsLCSd/fvn07SktLsXDhQtxxxx3o6+ubdh/BYBCBQGDSl504xSO77FihJUGrPf74oQrJ50umfMIBR/a3GRvdleV1YUUVu8F+l3y0hAlwu0F1fiZcSn3yKmxNRmJqUxgQd+Ns7HmGOBNdBW1vby8ikQjKyib7b8rKytDZ2ZnQPr7zne+gsrJykijeuHEjnnjiCWzbtg0//OEP8eabb+Kqq65CJBKZch/3338/8vLy5K+amhrlP5TB+MdC6JPGaNqpQsuHKzSQ5UB75AsNZdDOiIkZkZRHS5hJo3TeVZVwYJcKreSh9Y9NYDg4YfwCisyJCCTOxNItzv/+7/+Op59+Gs8++yx8vtjovhtvvBHXXXcdli5dik2bNuGll17Cnj17sH379in3s2XLFvj9fvmrpaXFoJ9APdw/W5rjQ47Puj6mj0JZtDqiMuy8Le0qtPWAwfFxF/CkA5oYRphAs+SfrS1QcdNqE0Gbm+FGttcFwKzGMBK0VkFXQVtcXAyn04murq5J3+/q6kJ5efmMn33ggQfw7//+73jttdewbNmyGd87Z84cFBcX49SpqT0sXq8Xubm5k77sAn9kzx/h2wUuaNsGxzA+MXXlnFCIyrG3aWM5KKhj4yknRoChdkMPzSu0+9sGMBIMG3psgmhRG9kF2MZyAMTZDsyI7pKbT+tZ1BlhGroKWo/Hg1WrVk1q6OINXuvWrZv2cz/60Y9w3333YevWrVi9evVZj9Pa2oq+vj5UVFj7TlIJ/JH97CLrTwiLpyjLixwfu2sm24HGcHGWk/yFZiISRfcQywbmj+pSFqcbKJjNtg22HdQWZqE6PxORqIg9TdP7+wlCD3iFtqYg9S0HAFAl3Zyb4qPNnyXdOI/G8sEJU9DdcnDXXXfhkUceweOPP46jR4/ijjvuwMjICG655RYAwObNm7Flyxb5/T/84Q/xz//8z3j00UdRV1eHzs5OdHZ2YniYiaLh4WF861vfwnvvvYfGxkZs27YN119/PebNm4cNGzbo/eMYTkzQ2qtCKwiCvGbeoEBohIrKSYdUnXU7HSjK8mq5Kmti4uNAHt/19umpIwoJQi9atLAcyFPCrF+h5U+b+PnNUEy8cSYmo7ugveGGG/DAAw/g7rvvxooVK7B//35s3bpVbhRrbm5GR0eH/P6f/vSnCIVC+PSnP42Kigr564EHHgAAOJ1OHDhwANdddx0WLFiAW2+9FatWrcJbb70Frzf1LtB8fOFsm0wJi2eWVB1opAqttvDKSW7ylZN4u4HDkXxCgu0wcdb6xXNLAQA7TpGgJYylWbIcKK7QhkaAoJ9t26BCW8EFrVmTKWXbAUV3mYnLiIPceeeduPPOO6f8s482cjU2Ns64r4yMDLz66qsarcz68AptnRovlEnUSRXaRqrQakc0DAxLnnQFHtq0yaDlFC9kr73HDT/0pfOYoH3ndA8mItHUHTNMWIrh4AQGRkMAgFqlgpbfNLuzAK/1e04qck2s0AKm3jgTMegMa2FEUZTFoN0sB0CsIYEsBxoy3MUaDxwuIKsk6Y+nTUMYp2QRe+0xXtCeU5GPoiwvRkMRvE8+WsIguN0g1+dGbobCZBzZ1lShKOvaaPj5rN2sLNpiczKvicmQoLUwXUPjGJ+IwCEIqFHjhTIJXlXmtglCA7ivLbscEJL/9U2bDFoOv9AMdQDjfkMP7XAIuESq0r5JtgPCIDTxz9qoIQywgOWARwTStDBTIUFrYRqkyK6q/Ax4XE6TV5M8dVIyQ2M/eWg1YyiucqKAtMmg5fjyYmkQJkzyuWw+E7TbT3ad5Z0EoQ2q/bOArSK7gMmWA9HgzGkAMQ9toJX5jwlTIEFrYexsNwBiloPOwDhl0WpFQF3lJO0sBwBQIl1sTLAdXDqPNb9yHy1B6E06V2hHQmEMjZuQ+5xZCGQUsm1qDDMNErQWxq6RXZyiLC+yPKzvsJl8tNoQUDtUQbIcpHoGbTzF3Ed7zPBDL63MR0GmB8PBMPa19Bt+fCL94OdadRm0yrOuzSDb65ZzzzsCJjWG0cQw0yFBa2HsnHAAsCxaagzTGJWVEz4aMi0rtCYkHUzy0ZLtgDCAlkE+VEGLDFp7VGgBKyQdcB8tCVqzIEFrYeQMWptWaIF4Hy0JWk0YUh52PhycQGB8AkAaNYUBpiYdALH4ru0nqTGM0B9eoVUc2QXE3Tjbo0ILxAlasyq0JVJEoAlPgggGCVoLI1sOiu1ZoQXikw6oMUwTeLOGggptu1Sdzfa6kONTGOdjR7igHWxm4ykN5rL5zEf79uluhMlHS+iIKIrqK7TRCDDUybZtVKGNRXeZJWgXs1eTbpwJErSWJRKNyvO46wrtW6GVLQcDVKFVjShqNiUsrcgsYl8QTXkcuKwqH3kZbgyNh/FB64DhxyfSh97hoNyAW630KcxIDyBGWCxgdpmGq9OXCjPH3wKxCm3fKSAyYc4a0hwStBal3T+GiUgULoeAqnz7ChB5Whhl0apnfDBWYVTwKDDtMmjjMdF24HQ4cMlc8tES+sOrs2U5PnjdCqMeeUNYVikb4GITTLcc5FWzyWrRCaD/tDlrSHNI0FoUbjeoLcyC03GWv6ZICKjfBhx5HjjyHDDYpP8CE4RXaCmLVgN4dTajAHAnf5OTdhm08RSb1xgGAJdKtgNb59GGg0DTu8CeR4CGNylv04LI/lk1jcQ2bAgDLGA5EBzkozUZ+9x+pRkJNYSFg8AHTwDv/Dfgb4l9X3ACyz4LXPwtoGiuziudGe6hbfePIRSO2HJAhGUIqIvSSVvLARBXoTXnQsMbw96q70EkGj37TaqVCI8Df/lXYO+vgHCcWHC4gOWfBzb+O+Cxr88/leAZtDVqnsLYLLKLE5sWZpKgBdh5pn0f+WhNwkZn1fQilkE7zYVi3A/8+nrglX9iYjarBKhdB1SsYP6nD38L/OwioPEt4xY9BaU5PvjcTohi7GRLKESFfxYA2gbTMIOWU2zecAUAWFFdgFyfG4HxCey3k4+2+wjwyOXArv9lYjarBJj7cZaDHA2zG+pHLgO6Dpu9UgKxKWGaVGhtMlSBY3psFxB343zUvDWkMSRoLUqDVKGdsiFspAd4/FqgeSfgzQOuegD4+iHglq3A7W8CX34DmHUh81s+9RmgYYfBq48Rn0VL0V0qoSlhyuEXmv7TzKJjMC6nAxfNLQEAvGmX+K6eY8CjG5mozSoBPvcM8M2TwM1/Ar5+GNj8Ivu32HsC+NVVQF+92StOe7Sp0NrVcsB+5sD4BEaCJkwLA0yPCEx3SNBalMbpKrQTY8CvPwl0HmAXmS+9BJx/G+Dyxd5TtYpddOZfyaoqv/ks0HXIwNVPhqK7NIJPCSPLQfLkVACeHPb0wiThxeO73jxlA0E73MVuhoN+oHoN8NV3gQUbAUFgfy4IwOxLgK+8DVStZu975vNAcMjcdac5mlRobWo5yPG5kOlhljbTs2j7TrInGIShkKC1KNOOvX3t/wJdB5mYvWUrUL5s6h24fMBnnwTmXM5E7fN/b1qUCA/4JsuBSlRUTkRRTO+UA0GIXWzMagzjE8NOdVk7jzYcBH57A+BvBgrnsspsdunU780qBm54CsguZxXd5/+excsRpiBXaNNsShjAngaabjvIrwXcmewpUH+DOWtIY0jQWpCJSBStUgh+XbygPfI88P4v2fYnfwYUzZt5Ry4ve58vH+j4EHj3v/VZ8FmoLWQn12bKolUHr5zkViX90f6REIJhJqL4ST/tMPlx4MqaQuRneOAfm8D7zf2mrCEh3noAaP8AyCgEbvq9lOE7AznlwGd/DTjcwNEXgCPPGrNOYhLhSFR+CpNuU8I4pjeGCY44vz4lHRgNCVoL0jIwgqgowud2ojxXshKM9AAvfo1tX/h11piRCNllwMYfsu03f2jKL1lNPlVoNUGFh5ZXZ4uzvcrzKe2OyRcal9OBjy9ktoPXjnWYsoaz0nUYePtBtn3Ng6xCmwg15wMX/xPb/ss9LBmBMJR2/xiiogi304GyHN/ZPzAVoWEgGGDbNqvQAhaI7gJMT1RJZ0jQWpBYQ1gWBO5Ze+P7LNmgfBlw+feS2+GyG4D5G9hjkL9+X+PVnh1eoeWh34QCwkFgtJdt5yZfOUnrDFoOv9D0njBtCVcuYiLhdSsK2miE3TRHw8DCa4Alm5L7/AVfY1W9wWZg18O6LJGYnhbpCVh1fiYcDkHZTvhNsycb8OZqtDLjMN1yAJCgNREStBaEN0/V8YawzoMsHgcArvoR4HQnt0NBAK64F4AAHH3R8IidGunxV3P/CETy1ymDPwZ0etmj4CRJ64YwTgkfrnCSiTcTuEIStDsbehEYs9h4zA9+DbTtZULm6gdiDWCJ4skCPn43297xAHuqRBhGsxb+WbkhzH7VWcAClgPAdGtTOkOC1oJMaggTReDVLYAYBc75W5Y1q4SSRcA5m9j2W/+hzUIThM8UH5uIoH/E+MiklED2tZUnLzQQsxxUpmMGLSevFnBlAJEgMNhoyhJmF2djXkkOIlERfz3ZacoapiQ8Duz4Edu+bIuipwAA2NOgihVAaAh47381Wx5xdniFtjYNG8I41rAc8ObTE6bdOKcrJGgtSEP8lLBTr7PhCC4fsP5f1e344m+x18PPGXr36HM7USp5uqgxTCEB5Q1hQOwRXEWuQm9dKuBwAsVSI6WJ1ZMrF5UDAF47aiHbwfuPsli43Cpg9d8p34/gAC7+Jtve+xjLwiYMIVahTb/ILo4lLAf5s2I3zgON5q0jDSFBa0Ea+yXLQUEmsEOqpq65jUWCqKHsHGDxJwCIwDv/pW5fScKDvqkxTCEqw847AqxJJ20TDjgW8LdduZj9Hb52zCIV2tAw8Nb/Y9uXfHtyprUSFl7DzlVj/cCB36lfH5EQ/NyqSYWWLAfKcTiB4vlsm3y0hkKC1oI09LIq5rKJw0DrbuabvOBr2uz8gq+z18N/AsYHtdlnAvCgb6rQKkTlhUau0KazhxYAiuMeB5rE5fPL4XQIONUzhIZeCwwb2f0IazgsmA2suEn9/hxO4PyvsO1dD1MurUHwc6smFVqllhOT4ZaDgdEQxkImDjagEbimQILWYoxPROS7y9nHfsa+ed4XWPyWFlStAkrPYZ65A7/XZp8JwBsVqEKrkCG1U8LIQwsg5m8zsXKSm+HGutnFACyQdhAJxRIJLv1O8g2n03HezYA7i13QG7Zrs09iRjSp0A7Zu0Kbn+GB18VkTWfAxOg4agwzBRK0FqOpn91lX5LRCk/Tm4DgBC78R+0OIAjAys1se9/jhlVPYtPCqEKrCBXNGqIoonNIshzkpbGHFoi70JxgjZYmweO7TM+jPfQnYLiTCZhzP6Xdfn35wIrPs+33H9Vuv8SUjIbC6BsJAlBZoZXPM/as0AqCYA3bAVVoTYEErcXgCQffydnOvrHss8xkriXLPstsDF0HgY792u57GniFtpkqtMpQ0azRPxJCSJoSVp6T5paDgtmA0wNMjAD+FtOWwX20246bOAZXFIH3fsK219zG/r9oCb9xPvGqofamdIRXZ3N8LuRlKKyyRyPAcBfbtmmFFojZDkxtDCvlmdfmRQSmIyRoLUZj3zBKhQCuEHezb6z9qvYHySgEllzHtvc9of3+pyDWFEYV2qQRRWBIaiBSUDnhlYrCTE/6TgnjON2xiWFdR0xbxupaNgZ3cCxk3hjcxrdYxrU7E1h9i/b7L1vKKlWRIHDkBe33T8g0S0/2avLjhvEky0g3IEZYUkV2qYarMxbe+GpqdFd+HSsahceBwSbz1pFmkKC1GA19I7gt4x24EQaq17BMRz04T6qeHPoj89HpDG8Ka/OPIRI171GvLRnti/0d5ZQn/fF2agibTOli9tptnqB1OiwwBnfXT9nr8s8rGtZxVgQBWPoZtn2Q0g70hE9h5FMZFcGjAbPLAIdLg1WZgxzdZXrSgbmjttMRErQWo7nPj6/43mL/seY2/Q5UdxGQXQ4E/cDpN/U7jkR5rg8uh4BIVESHn+a8J0VAagjLKlH0WLiDpoRNpnQJezXZ3yb7aM3Iow20ASe2su21X9HvOFzQNr4dE0yE5sRXaBVj84YwToUVLAdAzHZAgtYwSNBajJruHahxDiLoKUh+lnoyCA5g0bVs+8hz+h1HwulwoIpsB8pQeaHhlYq0z6DlcEHbba6g5WNw32s0YQzuB0+xprhZF8YqSXqQPwuo+RgAETj0B/2Ok+ZoU6G1d0MYxxLTwoBYRCAlHRgGCVqLcfU4q5oMLLoRcHn1PdiS69nr8ZeBiP4X1FhjGAnapAioy4aUBS1VaBncctBz3JB/99Mxuzgb86UxuNtOGDhkQYwCH/yaba/8ov7HW/ZZ9nroj/ofK03RpkLLG09tXqG1guUAoKQDEyBBayGCfU24RGC+Ps+aL+l/wFkXAJlFwNgA0PS27oeLRXdR0kFSyBVahYLWTxXaSeTVAJ5sIDoB9NebupSrzmF/py8dajPuoPVvAP5mwJcHLL5O/+Mtvg6AwBJVAgb+nGmEJhXaVLMcmC1o42+cKenAEEjQWojhPU/BIYjYEV6AgqqF+h/Q4WJjKgFDupApukshKiu01BT2EQSHJRrDAOAT51YBYII2GjVoota+x9nrshsBtwH/JrJKWIMrEPPtEpohimKsQqsqg9beU8I43HLQOxxEKGyikCyYDbgyWNLBQIN560gjSNBaBVFExuGnAQB/dl+mPHolWbjt4NiLut9FUnSXQlQ+CuSVCmoKi6NEErQmRncBwCXzSpHrc6N7aBy7m/r0P+BoH3D8FbbNc2KNYMFG9kqCVnP6R0IYm2Dn7up8qtAWZXnhdlpgWpjDGWsM6zps3jrSCBK0VqFlFzKHmzAsenGk4DLjjjv7UsCbB4z0AO37dD0Uj+4iy0GSqJwSRpaDKbBI0oHH5cTGJezv9cWDrfof8PCfgGgYqFgOlJ2r//E4C69ir6ffBEJ0Q6slvCehNMcHn5qc6RRpChMEAeW5bCKi+baDc9grCVpDIEFrFfY/CQD4ffA8lBYVGXdcpxuYexnbPvUXXQ9FTWEKUTElLDA+IVdvyHIQRxlPOjC3QgsA1y2tBgC8aISP9sAz7HXpDfofK56SxUB+LRuycHq7scdOcXiBoLZARXU2OASEhti2zSu0gEWmhQFAmSRou0nQGgEJWiswMQYcfg4A8Pj4x+TmKcOYu5691m/T9TD85+oZDmIsFNb1WCnDxCgw7mfbCiq0/ISe63Mj02PfsHTN4RXa/gb2/9hErlpSCadDwMH2QTRKo691ob8eaN3DPMRLP63fcaZCEIAFUpWWWx4ITeAFAlX+WW438OQA3hwNVmUulpgWBsQELVVoDYEErRU4+RoQGkK3oxg7JuZhVqHBgnbex9lr215gVL8xnAWZHmR62COx1kGyHSQEfwzozmTWkCRpp6EKU5NVAmQWAxBNz6MtzPLiwjklAIAXD+pYpT0gTeuaczmbBmU03Ed78lUWHUZoAq/Q1qip0MoNYfavzgJWSjqQBO1AIxDS8WaVAECC1hpIgeMvRM+HCIfxFdrcKlaxEqPA6b/qdhhBECi6K1niG8IUNApSBu0MWKh6wm0HL+jloxXFmN1g2Y36HONs1F0EuLOYX7/rkDlrSEF4hVbVdSNFGsI4lrEcZBVLN48i0E0Tw/SGBK3ZjPuBE68CAH4xdB4AlVmCSpkrVWl1th2QjzZJVDZqUEPYDJQtZa8WEFc8vuvNU936TA1r28uig9xZwKJrtN9/Ijg9LPsaMGTcdrqgbYXW3g1hHMtYDoC4yYTm3zinOiRozebYy0AkiHDhfOwar4QgqIxeUco8yUd76i+smqMTNVShTQ6NIrtI0E5BudTlbwFBu6AsFwvLcjERieLVo+3aH+Dwn9jroqsBj8FPgOKZcxl7bdhu3hpSDKrQnollLAeApZ4EpTokaM1Gsht01V4DQEBFbgY8LhXRK0qpXcd8msNdul7geScuCdoECai70HTQUIXp4bFVnYd0vYlLFF6l1dx2IEaBw8+y7SWf1HbfyTL7Uvba9C4QCZm7lhQgEo3KVUhVFVqV0withjz+1goVWn6esUCiSqpDgtZMRnrlCJtDRaxCarh/luPyMo8bADTs0O0wvEJLloMEGVZnOaCmsBkoWcim5QX9gL/F7NXgE0uZoH3lcDvCEQ2bplp2sUq/Nzf2JMYsys5hzXgTo0Dr++auJQXo8I8jEhXhcsSyVxWRok1h3cPj2v4uKYFbDrqsceOcypCgNZNjLwJiBKhYjiOhYgAm+Wc5dRez16a3dTsEVWiTRG2FliwH0+P0AMXSiGkL2A4umF2CwkwP+kdD2NnQq92OeXV20TXsxtVMBAcw+xK2TXm0quGFgar8TDgdKi7nKWY5KMn2wiEIEEWge8jEaWEAu3EWnMDYQOz/M6ELJGjN5Mjz7HXJJjRLAs/wyK54Zl3IXpve1S1WJ75CK9Ld6tlReaGhlIOzIPtozfe3uZwOXH0Oq8RrZjuIRoAjz7Fts+0GHG47aKDGMLW0yP5ZFYWQaJhZzYCUaQpzOhwoy2EV606zBa3LBxQvYNudB81dS4pDgtYsRvtij/aXXI/mfg2M/WqpWM6CtccHdatYcZ/XcDAMvx7d3KmEGI0JWgWPAkeCYQyNswEWVKGdBu5v67LGhYbHdz37YYs2N3zN7zKx4ssH5l6ufn9awBvD2t5nE6oIxTTLCQcqrhvD3excIziBrFKNVmY+FVaJ7gKAimXstfOAuetIcUjQmsWxl5ndoHwpUDhXm05VtThcQO3H2HajPraDTI8LRVnssWcL+WhnZqSXVU8gANnlSX+cV2czPU7k+GhK2JTEN4ZZgKvOqUSG24n63mHsbx1Qv8MjL7DXRdcwi4UVKKgD8mexf9vN75m9GlujydhbftOcXQY4TGhI1gnuKbZG0oEUEUiCVldI0JoFfwy4+HoAQJNUoTXVcgDE2Q7e0e0QsSxa8tHOCI/syioBnO6kP97uZ/9/K/MyISgYypAWlEsXmv7TQMj8G6xsrxvXSGkHv9vXpG5noggcf5ltL/qEypVpDD/PtJCgVYMmY29TrCGMY6mkA36eIcuBrpCgNYOx/ph/7JxPYiwURs9wEIDJTWFAXGPYO7r5aGPTwswXEJZmqJO9KrzQxIYqqOh+TnWySuIm+VgjVucz59UCAH73QbM620H7PiDQxoYpWMVuwOFPgqhCqwpNK7Qp0hDGsVQWbblkORhoYMOUCF0gQWsGx//MHreVngMUzZNPStleF/IzTH4sWLGcXQDHBnS7wFOFNkF45URhNmRHgDVDUEPYWZBtB9Z4HHjNOVXIcDtxuncY+1r6le/o2Evsdf4VrDHFStSuY69t71MerQo0rdCmSAYtx1IV2sxCIJf5463QgJqqkKA1A36hWcweAzbFNYSZ/mjY6dbdRxuL7qIK7YyoTTigsbeJwasnHdYQtFleF66VbAe//6BZ+Y74ecZqdgMAKJoPZBQC4XGg40OzV2NLxkJh9EpP9jQZqpBqlgMrVWiBuMYwsh3oBQlaowmNAPVvsO1F1wKI3WWb7p/l8OqJTv62WHQXVWhnJKDR2Fuq0M5MxXL22mkdYfXZlbMAAL/bp9B20HuCfTncrEJrNQQhdp5p3mnuWmwKf7KX5XGhIFPFk70hqtAaAjWG6Q4JWqM59RdWlcifJT/qbNbCB6UlNeez15Y9+uyehiskhsrKCU0JS5DKFey16zAQsUaU3NXnVCLT40RD3zD2NiuwHRx9kb3OuRTw5Wm7OK0gH60qWgal60ahyqbPQKpWaGM5tJbIPKfoLt0hQWs08Y8BpZOQnEFrlQpt1So20SfQGqsSakh1PhO0bf5Ra5xorApZDowhvw7w5jEvZ89Rs1cDgMXbcdvB7z5QkHYgn2eu1XBVGhNfoaXzQNLw60ZNvsrrhnyeSa0KbXkOO++FwlH0j1jAp82TDrqPkm9cJ0jQGklkAjjxKtteHLvQNFkhgzYeTzabuQ4Arbs13z2vGIbCUdkDRkyBXKFV2hRGloOEEIRY9cRCfs7PnqfQduBvZQkHEICF1+izOC2oWM6a1cb6mT2CSIr4Cq1iggEgNMy2U6xC63U7UShZMSzho82rZU9LohNAz3GzV5OSkKA1ksa3gKCfRQVVny9/u9kqGbTxVOtnO/C4nCiVxhK2+cl2MCUTYyxpAlBUoR2fiGBglFUBqEKbABUr2KuFBO1V51Qiy+NCU/8I3k/GdnBMyp6tWQtkW3jyk9MDVK1m2+SjTRq5Qqsq4UC6afbmskJGimGpaWGCENeAut/UpaQqJGiNhD8GXHi1PJElGhW1udPWGi5odajQAjHbQSv5aKeGV2ddGWxsaZJ0ShUJr8uhrmEkXbCgoM30uPCJpQqGLByT/LNWthtwuF+/7X1z12FDtMmgVdd4anX4zXznkAUELRA7z7TvN3MVKQsJWqMQo7HKSVyMTtfQOELhKByCgMo8CwnamjXstWM/ENbeFiAL2kEStFMi+9rKZa91MvCGsIq8DPOj4OyAnHRwUBo3bA3kIQv7mhKzHYz2A03vsu3FNhC0VavYa9tec9dhQzQZl56iDWEcS1VoAaDyPPba8YG560hRSNAaRdteYLgT8OQAsy+Rv81PSlX5GXA7LfTXUTAHyCxi5nUdujKr8tmJps0qJxqrEVDpn6WGsOQomsseuYbHgN6TZq9G5qollcj2utA8MIqdDb1n/8CJPwNihCWoFMzWf4Fq4ZaD7qNAcMjctdgIURTlCq26DNrUjOziyNFd0pAZ0+GCtvMgNYbpgIUUVIrDY3QWXAm4vPK3m/st1hDGEYQ4H632tgOyHJwFlY8C5YYwErSJITji/G3WsR1keFz42+U1AIBf7244+wfskG4QT045kFsFQCRfYRIMjIYwEmJPEvi5VBEqs66tTrk09tsyFdqC2cxCFglZZtR2KmGIoH3ooYdQV1cHn8+HtWvXYvfumQXS73//eyxatAg+nw9Lly7FK6+8MunPRVHE3XffjYqKCmRkZGD9+vU4edI6VZUzEMVpLzSWy6CNp1qyHejgo63Ki0V3EVMQ0CiyixIOEofbDiwmrDavnQMAeHpvE8YnItO/cYqhLbaAbAdJw5/slWR7keFxKd9Rik4J48QqtBYRtIIQq9K2k+1Aa3QXtM888wzuuusu3HPPPdi3bx+WL1+ODRs2oLu7e8r3v/vuu/jc5z6HW2+9FR988AE2bdqETZs24dChQ/J7fvSjH+F//ud/8PDDD2PXrl3IysrChg0bMD5ukccKH6XnGNBfz7p6502e2tNktQzaeKqlx4Ht+7TfdQF5aGdkWJ3lgIYqKEC+0Gj/710Nl80vRXV+JgbHQnjpUNv0b5xiaIstkAUtNYYlSqwhTOV1I5DilgOreWgBErQ6orugffDBB3HbbbfhlltuwZIlS/Dwww8jMzMTjz766JTv/+///m9s3LgR3/rWt7B48WLcd999WLlyJX7yk58AYNXZ//qv/8L3vvc9XH/99Vi2bBmeeOIJtLe347nnntP7x1EGr87OuQzw5kz6I8uNvY2Hd2QONgMjCfj3koCaws6C2gotWQ6Shwurjg8tMzEMAJwOB75wPvPDPr7r9PRvnGJoiy3gPto2a91IWJlYZJfKJ3tUoTUei944J8yfvw0c/D2LlrQYugraUCiEvXv3Yv369bEDOhxYv349du6cOndw586dk94PABs2bJDf39DQgM7OzknvycvLw9q1a6fdZzAYRCAQmPRlKDP42izroQVYCHTRPLat8WNY3hQ2NB5GYMw64sEycA8tDVUwjsK57N98eBzoPmz2aiaxWRK0fz7Sju6hKZ5ERUJTDm2xBZUrpMmEbTGBRcxILOpRxXUjMgEMS09KU7xCOxwMYzhoketM5Ur22n3UkqJwRnqOA7t/Bjz/92xAhMXQVdD29vYiEomgrKxs0vfLysrQ2dk55Wc6OztnfD9/TWaf999/P/Ly8uSvmpoaRT+PIkQRWPlFYO7HWf7sR7C0hxbQ7fFItteNvAw3APLRnoEoAkPSv+WcckW7oJQDBQhCrErbaq3H34vK83D+rCJEoiJ++37jmW+YZmiLLfBkAyWL2Tb5aBMiNvZWxXVjuAuACDhc7N9NCpLjcyNL8hhbxnaQW8X+f0fDQJe1bpzPCi/Ozb6UDeOwGGmRcrBlyxb4/X75q6WlxbiDCwKw+u+Am/90xkljJBhG3wjLeJ1VaNEpLRX6PR6hpINpGO2LRboosBxMRKLokUYKU4U2Sar0842rZfNMtoOjfGjLNfLQFltRJVWtLHYjYVU0qdDyanh2uT3/zSSI7KO1iu1gUmOY9c4zM3KcZ+lbc6S2roK2uLgYTqcTXV1dk77f1dWF8vKpK0/l5eUzvp+/JrNPr9eL3NzcSV9WgPtn8zLcyJWqlZaDX2h0MLBT0sE0cLtBZjFrJEwSPiXM5RBQnOU9y7uJSVi0QgsAN66qg9vpwAetAzjYNhD7AzEau9As/sTUH7Y6/P+73S7wJqFJhZY3hKWof5ZTYbXoLiBmO7BTI2SgXXqCIkz5tNkK6CpoPR4PVq1ahW3btsnfi0aj2LZtG9atWzflZ9atWzfp/QDw+uuvy++fPXs2ysvLJ70nEAhg165d0+7TqljaP8spX8r8bUMdmvvbKOlgGrjdQOGFhp+4y3Mz4HDYqDnICvAKbe8JYNxv7lo+QlG2F9eey0bhPhGfSdu6hz0+9uZOGtpiK3gDaucBZrkhpiUSjcoDaTSp0KZoBi2HV2g7rTJcAYhFYuqQ8a4b/Ka5eg2QXTbze01Cd8vBXXfdhUceeQSPP/44jh49ijvuuAMjIyO45ZZbAACbN2/Gli1b5Pf/n//zf7B161b8v//3/3Ds2DH8y7/8C95//33ceeedAABBEPD1r38d3//+9/HCCy/g4MGD2Lx5MyorK7Fp0ya9fxxNkSO7rOqfBZi/rXgh29Z4/nSVdKJpG7TQnbMVUBmlQw1hKsgqZrFXEC0Zq8NtB0/uaUA4EmXf5ENb5l+pqKJvCUqXAA43MDYA+A20hNmQDv84IlERLocgDw5QRIpHdnEsmXTAIzEHGoCRHnPXkijHuN3Auk2nugvaG264AQ888ADuvvturFixAvv378fWrVvlpq7m5mZ0dMQqfxdccAF+85vf4Oc//zmWL1+OP/zhD3juuedw7rmxXMVvf/vb+NrXvobbb78da9aswfDwMLZu3QqfT8UvtwlYOrIrHp38PhTdNQ0qKyfUEKYSCwf9X31OJYqyvOgMjOMvxztnHNpiK1xeoFRqDLPYYAur0SKPS8+E06HiEp7ikV0cS2bR+vKBkkVsu3WPqUtJiLEB1ngKWPo8Y0hT2J133ommpiYEg0Hs2rULa9eulf9s+/bteOyxxya9/zOf+QyOHz+OYDCIQ4cO4eqrJ/s1BEHAvffei87OToyPj+Mvf/kLFixYYMSPoilywoHlBa0+PlqyHExDQG1kF3u0VqGmepPOyLmo1hO0HpcTn1s1CwDwq/fq2fjMgQbA6QXmX3GWT1sceVKbdUYPWxFeCFH9ZE++caYKrSnYyXZw8jWWylC8ECiaa/ZqpiUtUg6sii08tMDk6C4N/W1yUxgJ2snIFxplkV3tUpNdpZqGkXSGPw5s3W1JP+etF7ALyrMftmLkw+fYN+dezuxBdoYEbULwKWE1Wk0JS/UKba4FK7RALF7PDhXaoy+wV4s3nZKgNZEmre609absHEBwAqO9mjaGcctBz3Bw5hn16YbKyglZDlRSsYJVPEd6gP4ZJnOZxIrqQqyZVYSJSBTDB55l37TwY8CE4Y1hHfsteSNhFTTJLhfF9KnQyrFdFmoKA4AaSdC272PVT6sSGmZjtQFg8XXmruUskKA1iUg0KuevWjaDluPOAIolS0fnQc12W5jlgdfF/gm2U3RXDJXeNrIcqMTljcXVNU89fdBsbr9wHuocvSgbOQlRcAALrjJ7SeopO4clqoz0AMNTD8khYh5aVRXaoB+YkM65KV6h5Y1zfSNBhMIWKpwUL2CTCSdGga5DZq9mek6+zqYn5s8CypeZvZoZIUFrEp2BcYSjIpwOARV5NhAe5UvZa+cBzXYpCIJcpaWkA4lwkA1WANRXaCnlQDm1UgSgRQXtjatm4fNZ7HdxsGQ1S2ewO+7MWKMM2Q6mRZMKbUC6afblsf/vKUxRlhduJ5M6loruEhwxv76VfbSy3eA6NhTCwpCgNQke2VWttlPVKPidmYaCFqCkgzPg1VmnF8goSPrjkWgUXUO8QkuCVjFc0La8Z+46piHb68aX81lV59mJVSavRkO4j1bjiMBUQpMK7VB6RHYBrHDCq7SWawyrkRrkWy0qaMPjrCEMAJZcb+5aEsAGSio14Q1hlo/s4uhQoQVY9AxAjWEy8XYDBXfD3UNBREURDkFAaY4NKv9WpXoNAAHoO2XNnEh/C2aPH0NUFHDP6Tr0DFmo8qSGcknQdlKFdirGQmF5rHVtoQYV2hS3G3As2xjGfbRNO63pG69/g3locypjcYYWhgStSWgWvWIUvEI70KjpBCWq0H4EjYYqlOZ44XLSr7diMgpY2D9gTdvBkecBAPudi9AazsHju6zXvKYISjqYEZ5wkOVxIT9DxRCNNGkI48QawywmaKvPBxwuINAKDDaZvZoziU83EKx/PbH+ClOUmA/KJhXazEIgt5ptdx3WbLckaD+CysguSjjQkNqPsddmC9oOJEE7Po+lG/z8nVMQrVjhSZZyaYBOoA0Y6zd3LRakZZBnl2dCUONnlG+cqUJrKp6sWOWz8W1z1/JRIiHg+Cts2+LpBhwStCYhj721i+UAACq099FW5dP420mojeyisbfaYdXGMH+r5LkTsGz9ZmR7XTjZM4Q3T3abvTL1eHOl0cMAOi3c+W0S3KpWk6/yupEmU8I4/HxoqaYwzqwL2WvTO+au46M07GBPY7NKYudCi0OC1iRsM/Y2njLuo9UuuosqtB9BbWQXVWi1g1doOz4EgkPmriUe/hiw9mPILqnFTavrAAA/e+ekeWvSEu7Xt3KUkUnEV2hVkUZNYYCFp4UBQN1F7NVqFVp+nll4DeBwmruWBCFBaxLN/TazHABxFVrt/G28KawjMIZINKrZfm0Lb9ZQ+CiwXRK0lVShVU9eDasWihFrVWkluwGWbAIA3H7RfADAH/e3oMuKF+xkKZNsByRoz0Cz6ZJp1xQmpRxYzXIAsKQDwQn4m4HBZrNXw4hGgGMvs+0l9rAbACRoTSEwNoHBsRAADe60jYRXTrqPMX+NFrvM9cHpEBCJiuiy4uMgo+GVk1yyHFiC2Zey19NvmrsOTqA9FiUmjaFcWVOI86XJYb94t97ExWlE2TnslQTtGcTG3qq4bkQmYskd6VKhtWpTGMBGVvPx8laxHTS/yyaD+vKBukvMXk3CkKA1AW43KMz0INvrNnk1SZBXy4K4oxNAz3FNdul0OOTHQWlvOxBF1RVashxozBxJ0DZsN3UZMvwxYM1aILdK/vadl7JJfj99+wQmIjZ/0sErtN3HrD0S1ARi6TgqKrTDnQBEwOFOjYEcCcDPh11D49Z8Emg128ER6Tyz6BrAaR+NQoLWBOSTkp38swDLRZUvNkc02y1vDEt7QTs2AERYxqTilAOq0GoLr9B2HbJGHu2R59irZDfgfPa8WSjJ9qJtcAzPH2g1fFmaUlDHqlaRINCbIr5gDRBFUZsKbSAuScUGUUxaUJrjgyAAkaiIXinH11LM4oL2LXPXATC7wVHJ1mSTdANOevxrthia+aDMgGdzavg4kMbfSnC7QUYh4Ep+KEI0KspdvOSh1YiskthNXMMOc9cy1BGLEPvIhcbrduL2C+cBAH78pjZPT0xDcJDtYAoGRkMYCbGKNT9nKmIovSK7AMDldKA026LTwgDWgOpwsSzafpNtQ01vA8NdzG4w92/MXUuSkKA1ATmyyy5DFeKRLzTaZdFWUdIBY6iTvSr0z/aNBOXHzWU0JUw7Zl/GXk9vN3ERAI6+CEBkU8zyqs/4469etABOh4Adp7pxoG3A+PVpCb+J0DBRxe7w6mxJthcZHpfyHaVZQxin3KpZtADgzYlFY516w9y1HPw9e11yPeBUMbzDBEjQmgAfqmCryC5OqSRoNbQcyBVaf5oLWpVh57zyUJzthcdlj5gVWyD7aE1uDDv8J/b6EbsBp7ogE3+7vAYA8JM3Txi0KJ2gpIMz0MQ/C6RdZBenIo9XaC3afDz34+y1/i/mrSEcjPn0z/20eetQCAlaE7CthxYAShez16EOYFSbST5yFu1AmgvaIWoIsySzLog9DhxoMGcNA41SdJgAnPO3076NN4c9uacB/SMW9AomCmXRnoEm/llA9XnGrlh2Whhn3nr22rCDCUszqN/Ghilkl8cGPtgIErQm0GRnD603JzbJp1sb2wE1hUkENIrsIkGrLZ5slioAACdfN2cN/DHgnEtn/Pdx8dxSLKvKx9hEBL9677RBi9OB0iUABObls0IzngXQrBAiWw7SrEJr5eEKAHsqkV0GTIyal3vNzzPn/K1thinEQ4LWYMKRqNz8ZEsPLRD3OFAbQRuzHIylxjx6pWhUoaWGMB2Yv4G9nviz8ccWReDA02x76Q0zvlUQBHzt0oUAgId2nLBmRFEieLJY2gEAdB81dSlWQa7QqmkIA9KyKQyIH39rUUErCLEqbf02448fDADHpfPbUvvZDQAStIbT7h9DVBThdjpkk7rtkJMOtBG0lXnsBD0+EUH/iDYDG2yJygsNnxJGkV06sPAq9tr4tvFjcNv3AX2nAFeGPExhJj6/ug4FmR409A3jlcPtBixQJ7i9qeeYueuwCJpUaOOzrtOsKczyFVog5qM9ZYKgPfwcEB4DiuYDlSuNP74GkKA1GH5SqinIhMMhmLwahfCkA40sBz63E8XZXgBpbjuQUw7UNYWR5UAHiuYDhXPZhLx6g7uQD/yOvS66hll+zkKmx4Vb180FYPPmsJJF7JUqtAA0qtCODzLRAqRhUxj30Fq0KQwA5lzOYuu6DwN+g/OkP/wNe11xE6sW2xAStAZja/8sRxa0RwFRm0eaVXlpnnQQCakeR0lDFXREEIAFUpXWSNtBJAQcknxtSz+b8MfuuHg+BAF47VgHjncFdFqczpTwCi0J2kg0Kt/sq6rQcluTLx9wp9d5Ir5Ca1lrW2YhUH0+2z72snHH7a9nvl3BASyb2dZkZUjQGgwfqmDLyC5O4RwW/D8xqlnXd7XUGJa2wxV4ddbpATKLFO2CVx4qcimDVhcWbmSvJ19j03SM4NjLwGgfs6HM+3jCH5tTnINrz2GjcR/aYdMqLbccdB9lj8rTmM7AOCJRES6HgHI1v98qG0/tDL/RH5+IwD82YfJqZmDJ9eyVT+sygv1SdXbu39j63wYJWoPhGbS2bQgDWIRRCWs80cpHm/bDFeSGsHJFj3tEUZQrtNyTTGhMzccAXx4TmK17jDnmvsfZ64qb2O9dEvDmsMd21WNo3MIX8Okons8qRuODLO0gjeGFkKr8TDgdKi7baRrZBTBrW34GGxRgaR8t98k3vQsMd+t/vGgE+PC3bHvFTfofT0dI0BqMbDmwc4UWiDWGdWvTsFElj79NU0EbUBd2PjgWwvgEqxqS5UAnnG5g/pVs+4gB1ZOBRuD0X9n2eV9I+uMfX1iOhWW5GBoP4/FdNozwcvnY0yBA00EudiQ2VEHlzWogfQUtEDdcwapZtACQVwNUrQIgAsde1P949W8AgTZ2s77wav2PpyMkaA1Gs2kvZsMbNno1ErSSCEtbD+2Qus5jbjfIz/DA57ZffqBt4EMNDv9Rf9vBB79mr3Muj0VYJYHDIeDOS9ighZ/sOIFo1IaP7SnpAED8UAWNpoTZ+LGyGmyRdAAAi69jr3xql57seYS9Lr+J3UTaGBK0BiKKolyhtbWHFogJ2p7jmuwuVqG1+IlGL7igzVaZcJBn7xOS5Zm3HsgoYI/A9RyFGw0D+59i2yu/qHg3X1w7Bzk+F453BfD6sQ6NFmcgJXE+2jRGM6taGlsOgPikA4tfZ7igbXiLWZz0YqCR9QQAwJpb9TuOQZCgNRD/2ASGg2EAGowvNBvuoe09wS6+KpErtOlqOVBdoaXILkNweoAln2TbfKqOHhx5nv2byCphcV0KyfG5ccvHWITXj9/U5ubTUOQb53Sv0PK4R7VTwqhCC9igQls4ByhfBogRfe1N7z8KQGRPgYrm6XccgyBBayC8Oluc7UWmJ7kGD8uRP4sFvUdC7C5PJdWSwO8fDWEspF4g2w7Z26YusoumhBnAMik+6+iLwIQOF0ZRBN57iG2v/jIT0SrgtoOXD7fjZLfNIrziLQdpnHRAFVpt4MOMLF+hBWLxWdx6pDXh8di+19ymzzEMhgStgaSMfxZg3ccl7EKpRfUkP8ODDMn72WaHk43WyN42lRVaErT6U7MWyKsFQkP6ZNK27gba9gJOryaPAeeX5uLqc9iNku0ivIrmsXSHYIA1rqQpmlRo47Ou07ZCKzWFBSw8XIGz7EbA4WaTAjsPar//g78HxvpZE9qCjdrv3wRI0BpISmTQxqOhv00QhPRNOogfR6l27C1ZDvRHcMRmnX/wpPb73/kT9rrss8xyoAH/KEV4/eq90/aK8HJ6Yo9C09R2MBYKo2c4CACoLVRRoeVZ1w634qxru8Nv+DutbjkAgKzimN1o3xPa7jsaAd75L7a95jbAkRqNxCRoDaRJq+gVqyD7aDVqDEtXH+2kcZQ09tYWnPcFAAJQv435yLVioAE49hLb/tg/aLbbKxZVYEFpDgLjE3jCbhFe8gjc9Izu4tncWR6XnKOqiHi7gZCel37beGg5vCH04DPa2puOPg/0nWIT41b/nXb7NZn0/FdtEs393AeVKhVabRs25AptulkONBhHSZYDgymcAyyURuHueli7/e74DzZOet76mH9UAxwOQR608GO7RXjxzOs0rdDK/tnCTAgKhq7IBNTZmlIBfn70j03Yo1djzmXM3jTu1y7CSxSBtx5k22u/CnhztNmvBSBBayDcQ5s6lgOeRXtSk0zOqvw0rdDKCQfKfW3UFGYCa+9grx/+FhgbUL+/3hOxiT2X/X/q9/cRbBvhJVdo0zO6S/bP5qvNoE3vhjAAyPW55V4NW1RpBQewcjPbfvfH2jRGnnwN6DoIuLOAtV9Rvz8LQYLWQOSmsFQRtPmzWBBzeBwYbFS9u+p0rdCq9M8Ojcfi4KhCayB1FwNl5wITo9p0Iv/1B6w6u/BqaVKQtuT43Pg7O0Z4yUkHx9n/nzQjvkKrCpXTCFMBQRDsk0XLWXMr4MlmIlRtE2o0ArxxL9te/XdARqH69VkIErQGEQpH5MadlPHQOpxs3jqgyQjcqrw0bQpTWTnhJ+ZsrwvZXrdWqyLOhiCwR3YA8N7/MmGrlI4PgSPPARCAy/+vFqubkn+wY4RX4RzWHDYxAgw2m70aw2nRKh1HZdZ1qhDz0dog6QBgopPHau34kboq7QdPAF2H2Jjbi76hzfosBAlag2gbHIMoAl6XAyXZKTTNiScdaOBv4x7a1nQTtCrDzqkhzESWfhbIr2Vi4d2fKNuHGAX+/C22fe6nWNVXJ2wZ4eVwAcXaRQTaDZ5frnoYj3zjnL4VWiBmy2q305j1dXcC7kyg/QPg1F+U7WPcD7zxfbZ96ZaUTLogQWsQ8Rm0DocKY7/V0DDpgHtoO/xj9mpaUYtGFVqyG5iAywus/1e2/c5/xv4uk2HPL4CWXYAnJ7YvHbFlhFca+2g1G5dOTWEA4gWtTSwHAIvw4mkEb/wrEFHwe/vmD4HRXnZzuObL2q7PIpCgNQh+UkoZ/yxHwwtNeW4GBAEIR0V0D9vkcZAWDGlToaWGMJNY8kmg+nxmOeAVkETxtwDbJBG7/h4gr1r79X0EW0Z4yYkq6SVoRVGUPbSqBK0oxnJo07gpDLCpoAWAC7/OknA6DwLv/ndynz39V2aLAoAr/w1wpqY1jQStQTSnWgYth1sOek+oTjpwOx0oy2F2jLTy0coXmnJFHyfLgckIArDh39j2/ieB468k9rlICHj2q0BomE0fW61+Klgi2DLCi0d3pVmFtntoHOMTEQhCrGlWEeMDqrOuU4VKqVfDdoI2qwTY+EO2/eYPE/9dGO4Gnv0KABFY+SVg/hV6rdB0SNAaBM+gTZnILk7BLDaiMzwO+NU3bMhJB4M2O9koJTLBTjiAYm9bO1kOzKd6DXC+FIHz7FdYlN1MiCLwwteAprdZB/Mnfmxo2H18hNdfjncadlzFlPKIQPU3znaCP9mrysuEx6VimhO3G2QUKs66ThW4tc1WHlrOshuA+RvYzfBzXwGCQzO/PzwOPHsbMNzFbgo3/rsx6zQJErQG0axVp6rVcLi0TTqQo7tseLJRwnAXAJGNo8wqVrSLDhp7aw2u/AFQuw4IBoBnPi/93U6BGAW2/Qtw4GlAcAKfeTzmRTeIHJ8bX1rLIrx++pYNmsPy6zSNCLQL2vlnecJBlcoV2Z/KPBsXTQQBuPa/mPWg40PgqU9PL2pDw8BvPguc3g64MoBPPZryNzMkaA0iZT20gKYTw/j429aBNBG0ckNYueIKnWw5oAqtuTjdTJzmVLBK4sMXAvVvTH5PoB349abYHPVrHmRTwUzgqxexG9EXDrZZ//fN4YwlHaSR7aBRM0Hbxl5VDG9JFbiHNjA+geGgTZoi48mtBG7+E+DNA1reA578JNB5YPJ72vYCj38CaHiTPQH6/O80nTxoVVxmLyAdYMb+FPXQAnETw7RIOkiz4Qpy2LlyX1uHnzXQUVOYBcguAza/APz+i0D3EXaxKTsXqDwP6G8AWvcAkSCL4LnqR8B5XzBtqUsq8nDpvFK8eaobj7x7Cv96zTLT1pIQpUvYhbv7KLDoWrNXYwjaJxyQoM3xuZHtdWE4GEaHfwzzS23YIFW1Ctj8HLs5bt0D/OxiNiY3q4T9XTe9w96XUQDc9EddBrVYEarQGkD/SAijIeb7qkk1ywGgadJB2g1XUBl2PhYKY3AsBIAsB5aheAHw5TeAVbew/+46xCaJNb3NxGzlSuD2HaaKWc4dF7Mq7SPvnMJExOJTuHiFtu8s/uQUQjtBK1Vo0zyDllOVCr0alSvZeebcT7One6e3Awd/z8Ss4ARW3ATctj1txCxAFVpDaJKqs2U5PvjcKoz9VqUkrmFDjKpqbuGG/bTx0KoMO++Upt343E7kZdiw0pCquDOY1+2yLUDze0DHfjaAoe5ioHAu88JZgE8ur0FZjg8dgTE8f6AVnz6v1uwlTQ/3GffYaGyvSjQTtPKNMwlagD3NOt4VsGdjWDxF84BP/ZKdZ06+zq6/Lg8w7wqgoM7s1RkOCVoDaE5l/ywAFM6WRlOOstGUKn6RUuLOORniPbQK4CfkitwMCBYRSUQc2WXAkuvZlwXxuJz48gVz8YNXD+Onb52wtqDlFdrekywlIg3+vZPlQB9sm0U7HUXz2FeaQ5YDA0hp/yzAkg6KpKQDldUTHttlW8N+ssgeWqVDFViFtiIvhcYpE4Zy+4Xz4RAEvHGiC8e7AmYvZ3oKZrNzzcRIbBhJCjM4GkJAmuSmOh1HFrSUcgCkoKAlAJCgNYSUzaCNR34cqC7pIMfnRo6PPThIiyqtSg8tj+ziUTQEkSy1hVm45hx2Q/Xw2xb2pzrdQOEctt1rg6gxlTT2DwMASrK9yPKqeJgaGgaCfrad5mNvOXKvht0tB8QkSNAaQFOqZtDGo2l0FzvZtKZ6Y5goxvIhFVdoKYOWUA9vDnvsvdMYDYVNXs0McNtBT+oLWs0zaD05gDdX5apSA6rQKmN8IoJrf/pX/MMzuxEKW2/ACQlaA0h5Dy2graDNT5Okg6CfPT4FVFdoK3LJckAoZ8PiSswuysbgWAjP7G0yeznTw61NaVCh1TzhgPyzMiRoldHcP4KXD7fj8V0NcDutJx+tt6IU5LtXnoMffGI5zqsuMHsp+vHRhg0V8OEKKS9oJ42jVGYZoLG3hBY4HAK+chFrKvnZOxa2HcjRXSRoE4Yaws6gMi4eUlR5vUon+L/JuqIsSzYhk6A1gE8ur8H/t+Fc1BVlm70U/Sicw+K6QkPTj/xMkLQZrqBBowZZDgituOVjc+FyCNjV2IfDHYNmL2dqZK8+CdqEoQrtGfAKbTAcxcBoyOTV2Adun7RqPxAJWkIbXN5YXJfKx4HV6WI50OBCwwUtTQkj1FKa48MnllYDAH75br3Jq5kGHk003AmM+81di85onkFLQxVkvG4nirK8AMh2kAyNfaxRkQQtkfrItgN1gjY2XCHFTzQqHwWGwhH0DgcBkOWA0IZb180FAPx6T4Mlmz7gywOypczmvlPmrkVnGslyoCvko00ezW6ydIIELaEdcsOGOg+enHIwkOIVWn8re1VoOegaYhm0bqdDrjYQhBo2LK5AZV4GeoeDeOFgm9nLmZoSnnSQuhPDRoJh+WZVO8sBZdDGU5kuvRoaIgtaiyY2kaAltKNYErQqGza4h7ZraBxhq8+XV8OQusoJryyU5/osadAn7IfL6cCX1rKs10d3WtV2oM2TICvDh/Hk+tzIz/Co25lcoaUM2nj4dYYqtIkTawqzZj8QCVpCO2TLgbpHgaU5XjgdAqKiiE5pElZKovJRYCyyi+wGhHbcItkOXj3aYc2nJHLSgYXTGFQS/2hX1c1qOAiM9rJtqtBOImY5sOC/cQsSjkRlGyBZDojUh19o/M3AhPKThNPhkEVaSk9yUZlyQA1hhB7MK8nBpfNKERVFPLbLglXa4tTPotW8IczpZfGAhIxsOaAKbUK0Do4iEhXhcTlQlmPN3HMStIR2ZBbFTpoqGzZSPukgGGBfgPoKLQlaQmNuvYBVaR/deRrRqMVyOnl0V/9pIDJh7lp0QpcMWrIlTYKawpKD/5usLciCw2HNf0u6Ctr+/n7cdNNNyM3NRX5+Pm699VYMDw/P+P6vfe1rWLhwITIyMlBbW4t//Md/hN8/OZ5FEIQzvp5++mk9fxQiUbROOhhM0ZMNH0fpywM8yvxIlEFL6MWnVtQi1+dGQ98wtp9UlyutOTmVgDsLiIaBgQazV6MLmsUjUcLBtPDmY7IcJIbVEw4AnQXtTTfdhMOHD+P111/HSy+9hB07duD222+f9v3t7e1ob2/HAw88gEOHDuGxxx7D1q1bceutt57x3l/96lfo6OiQvzZt2qTjT0IkTLFGSQdShbY1VSu0GnQeU4WW0ItMjwufX10HAPil1ZrDBCHlbQeaBdirbDxNZXiFtjMwjkg0hZuPNcIOgtal146PHj2KrVu3Ys+ePVi9ejUA4Mc//jGuvvpqPPDAA6isPPMX7Nxzz8Uf//hH+b/nzp2LH/zgB7j55psRDofhcsWWm5+fj/Ly8oTWEgwGEQwG5f8OBAJKfyzibGhVoc1LcQ+tBpWTdmoKI3Tk1nVz8fDbJ/HH/c14aHQN8jNVdttrSclCoGM/i+5adK3Zq9EczS0HNFThDEpzfHAIAiJREd1DQSoMnAU54cDCgla3Cu3OnTuRn58vi1kAWL9+PRwOB3bt2pXwfvx+P3JzcyeJWQD4h3/4BxQXF+P888/Ho48+OuM85vvvvx95eXnyV01NTfI/EJEYPItWZQeyPP42ZS0HUoVWxYWGmsIIPVlVW4illfkIhqP4w/5ms5czGY3OM1YkFI7IN6uUQasfLmesuYlsB2fHDhVa3QRtZ2cnSktLJ33P5XKhsLAQnZ2dCe2jt7cX99133xk2hXvvvRe/+93v8Prrr+NTn/oU/v7v/x4//vGPp93Pli1b4Pf75a+WlpbkfyAiMeKju0Tlj3G4vyllm8JUXmgi0Si6h2hKGKEfgiDg5jV1AIAnd1vMq6rRkyAr0jo4ClEEfG6n+m5ynnJAloMp4b0a1Bh2djSbXKcjSQva7373u1M2ZcV/HTt2TPXCAoEArrnmGixZsgT/8i//MunP/vmf/xkXXnghzjvvPHznO9/Bt7/9bfzHf/zHtPvyer3Izc2d9EXoRMEswOEGwmOxSVgKqC6QBK1/dMbqu21RaTnoHgoiKopwCAJKsmlKGKEPn1tdB0EA3jzVjWbpgmYJuKDtOQGk2Pkh1k2eqX5gCjWFzUhlqhdONCIaFeVhHyklaL/5zW/i6NGjM37NmTMH5eXl6O7unvTZcDiM/v7+s3pfh4aGsHHjRuTk5ODZZ5+F2+2e8f1r165Fa2vrJJ8sYRIOF1DEIn/UVE+4h3Y0FIF/LAWjedQOVZDsBmU5PjgdlL5H6ENNQRYum18GAPjN+43mLiaewjmA4ARCQ8BwYk/87EJjn0bCIRoGhqT/NyRop4SiuxKja2gcoXAUDkGQ7YBWJOmmsJKSEpSUlJz1fevWrcPg4CD27t2LVatWAQDeeOMNRKNRrF27dtrPBQIBbNiwAV6vFy+88AJ8vrM/ctm/fz8KCgrg9VKlyhIUzwd6jrGkg3nrFe0iw+NCQaYHA6MhtPlHrdWQogXccpBXrejj7VJFgewGhN7cvGY2/nqiC7/e3YDvXLHEGmOWXV6goA7or2c3zjmpM9ZVM6/icDcgRpjwzyo9+/vTEBK0icH/TVblZ8DttG4BRbeVLV68GBs3bsRtt92G3bt345133sGdd96JG2+8UU44aGtrw6JFi7B7924ATMxeeeWVGBkZwS9/+UsEAgF0dnais7MTkUgEAPDiiy/iF7/4BQ4dOoRTp07hpz/9Kf7t3/4NX/va1/T6UYhk0WjWOq/SWnL8phpCI8D4INtWXKFlI4GpIYzQm0+tqIHX5cCRTj8+bBswezkxUtRHq/mUsJwKwOFUuarUhFcbSdDOTFM/y0W2csIBoHMO7VNPPYVFixbh4x//OK6++mpcdNFF+PnPfy7/+cTEBI4fP47RUSZY9u3bh127duHgwYOYN28eKioq5C/eyOV2u/HQQw9h3bp1WLFiBX72s5/hwQcfxD333KPnj0IkQ7HGSQepdrLhFxpPDuBV5uemoQqEUeRlePCJpexJwpO7G81dTDzxPtoUQrMMWrnxNHWq11pTmerxkBphh4YwQMccWgAoLCzEb37zm2n/vK6ublLDz2WXXXbWBqCNGzdi48aNmq2R0AG5cqJVdFeKnWx4s5wKXxsNVSCM5OY1dfjDB8347d5G/HDTCmv4tvl5pi/FBK0eY2+JKSHLQWLYIbIL0LlCS6QpRfPY63AnMO6f+b0zUJ2qglaDC02sQqsy1ocgEuCqJZUozPSg3T+G7Se7z/4BI0jBCm0kGkWLZLHSrEKbQxm008EFbe9wEMGJiMmrsS4xQatsTLtRkKAltMeXB2RLSRZ9pxTvJjYtLMXunmnsLWEzPC4nPrtyFgALZdIWSzfOQ+1AaNjctWhEh38cE5EonA5BzuJWDGXQnpWiLC88LiaDeJGAOBOq0BLpjQaz1lPWcqDh2NtKtRc9gkgQPmThjx82YzQUNncxAJBRCGQWs20VN85WoqGPCfPagiy41HaTy+cZ8tBOhyAIZDs4C6IokqAl0hwNfLR8iktrqgnaIXWCNhoV0UlNYYTBXDCnBHVFWRgaD+OFA8qHpmiKfOOcGoL2dC8TtLOLNBAONPY2IUjQzszAaAjDQXYDW1tg7QIKCVpCH+RZ62qGK7Bfnp5U8zepvND0jQQRjooQBKCMPLSEQQiCgJtW1wEAntnXZO5iONyvnyLRXbxCO6dYpVdRFIEAWQ4SgT/laqekgynhCQdlOT5keHTNEVANCVpCH+TKifIKbXF2ivqb5EeBygQt/39RnOW1dMg1kXpwH+2fj7QjYIUJfnLSgbpEFatwmgvaIpWCdqwfiEiTM1No6IQeyL0agyl0jdEQu9gNABK0hF7IF5p6NoJRAYIQa4xIGR9teBwY7WPbSocqUEMYYRJLK/OxoDQHwXAULx2ygO2gSJvMa6sQsxyoFLT8KVBWCeBMsSmLGhOzHKTINUZjSNASRF414MoAohPAgPLHk9xHmzJJB/xC484EfPmKdhFrCCNBSxiLIAj4zHm1AIDff9Bs8moQN8SlHhCj5q5FAzSzHFAGbcLELAcpco3RGBK0BCE4Yv42FdWTlKvQxl9oBEHRLmhKGGEmnzkvZjsYGjfZdpA/C3C4gInR2M2iTRkLhWVRpV7Q8gxaErRnI2WbjzWCBC1BALGcSFXRXSnmb9JiqIKfBC1hHsuq8jG/hNsOTBaRTjdQOIdtq5xMaDZcOGR7XSjK8qrbmV/6e8mrVrmq1Cc2wGfsrJNK05FG6alBHQlaIq0pUt8Yxiu0KXP3rMVQhQB5aAnzsJztQPbR2ju663Sc3UBQ+PRGhiK7EobnnY+EwvBbodHRYlCFliAATS40MQ9tqghaqUKr4lEgfyzJT8QEYTSfWckErSVsB3J0l70rtLwhTHXCAQD4pYa9vBr1+0pxMj0uFGayxrmUKZxoxHBwAv2jIQDWH3sLkKAl9KRYfQdyVdzjoJRAA8sB9xNTUxhhFsurCjCvJAfjExHzbQfF6jOvrQBvCFOdcAAA/hb2mkcV2kSoLkixJ4Eawauz+Rke5Ga4TV7N2SFBS+gHr5yM9ADjg4p2wf1N7f7R1PA3BdR526JRER2BcQAkaAnzsJTtQJ5KaHPLQa9WQxWisRtn8tAmBL/OkKCdjJ3sBgAJWkJPvDmxUG+FFxsu2oLhKPpGglqtzDxUVmh7R4KYiEQhCEA5NYURJvLZONvBcNBE2wG3NgVagdCIeetQyWmtIruGu1lcouCgoQoJIgvaARK08ZCgJYh4VEZ3eVxOlGSzjl/b2w7CQWCkm20rbNbg4d+l2T6aEkaYyiTbwUETbQeZhUBGIdvurzdvHSoQRVE7ywH3z+ZUsEgz4qxQhXZqGvtI0BJEDC2SDlLlZDMkzVZ3emMX4CThop7sBoTZWMt2wM8z9vTR9o0EMTTOJiqqjkcK8IYwshskCgnaqYndZJGgJQhtGsPkWds2P9loMFSBEg4IK8EF7StH2jESVDbiWhPkG2d7+mi5f7YyLwMZHpVVVT8J2mQhQTs1mk2uMwgStIS+aBLdJSUd2H00oSxolXcec8sBVWgJK7CiugB1RVkYn4jg9WMd5i1EgxtnM9E24YAyaJOFUg6mht9oafLv0gBI0BL6wqeF9dUD0YiiXcQmudj8ZCNH6SivnHDLAVVoCSsgCAKuX8r+PT9/oNW8hRTZW9BqlnAAAAF+nqEM2kTh1xj/2IT5ucoWITAWy6AlQUsQAJBXyzyjkSDgV+azi1Vo7S5o1T8KpAotYTWuX8b+Pb94qA3hSNScRRTHWQ5sGO8nJxxoOVSBKrQJk+NzI9fHclZtXzjRCP7UoDjbixyf9TNoARK0hN44nHGz1pXZDmIeWptbDrSo0PqpKYywFhfPLUVBpgd9I0G829BjziIKZrOO/okRYKjdnDWooEHqJtfUckAe2qQgH+1kTmtpgzEIErSE/qj0t1WljOVA/ThKuSksjywHhDVwOR249lxWDTTNduB0AwV1bNuGI3A1sxyEx2PRgGQ5SAoStJNp0HIUs0GQoCX0R2V0V1U+q0b2j4YwFjKxk1otKgXtRCSK7iGaEkZYj3gfrWkT/Wzqow1HomgeYBVa1YKWTyJ0ZQAZBSpXll5QY9hk7BbZBZCgJYxAZYU2P8ODDLcTgI2TDsb9QNDPthXOV++Qfna304FiadgEQViBDUsq4HU5UN87jMMdfnMWwYe42Cy6q2VgFJGoCK/LgQq10//i7QYKowHTFZoWNpnY5Lock1eSOCRoCf1RGd0lCIL9kw54dTajAPAoq8K0x/lnBbpYERYi2+vGxxeWAzDRdlC8gL322Wu4AhcOdUXZcDhU/l7LT4GoISxZyHIwGarQEsRU8OiuoQ4gOKRoF7b30Wrgn+U/O9kNCCuyaRn7t/38QbMErT2HK5zW0qvILQe51BCWLNWSta3V7s3HGsBGMWvYqGgQJGgJ/fHlA1klbFthlZb7aG1rOfCrz4akKWGElfnE0ioIArCnqc+cG0/+JMjfAkzY5zyhaSVMg/NMukIV2hidgXGMT0TgEATUqh3FbCAkaAljUGk74F399q/QqonsogotYV3KczOwdlYxAOAFM6q0mUXs5hki0F9v/PEVEks40MCrSJYDxfCmsL6RIEbt3HysAfwmq6YgE26nfWSifVZK2Bu5YUOZv61KfhxkV0GrPoO2nTJoCYuzabmJU8MEIeajVXieMYNY842GlgPKoE2a/AwPsr0uANQYpunkOgMhQUsYg9osWrlCa59HiZOgDFoiDeDxXW+c6EJgzIQRojb00WpmORDFuClhJGiTRRAE1BawvwMeo5auNNhwqAJAgpYwiiJ1Fxr+OMi242+pKYxIAxaV52FBaQ4mIlH8+Uib8QvgT4JskkU7ND6B3uEgAA3EQ9APhJgQIcuBMmqk60wzVWgB2GuoAkCCljCK4jgPrZj8vHdelezwjyEatdms9mg4No5TA8sBNYURVuY6qUr78mETRtDabLgCr4QVZXmRl+FRtzN+05xZBLjpHKEEXqFtoQotAHtFdgEkaAmjyJ8FONxAeCzm80qC8lwfHIKAcFRE9/C4DgvUkUA7E/FOD5BdqmgXw8EJBMbZI1yq0BJW5ppzWHXwz0faEYkmf/OqCtlDe5I9grc4vBKmTcIBtxtQdVYptYVUoQXIckAQM+N0A4Wz2baCEbgupwNlOT4ANkw6iL/QCMp+5dol73C214Ucn1urlRGE5lw4twS5Pjd6h4N4v7nf2IMXzgYEJ3v0Ptxp7LEVoGnzjQa2pnSnJl/y0Panb4U2FI6gRbrGUlMYQUyH2ugunkVrt8YwDSK7yG5A2AW304ErF1cAAF4+ZLCP1ukBCmaxbQU3zkZzWstKGFVoVcMrtC12K5poSPPAKEQRyPQ4USoVkewCCVrCOOToLoVJB3YNvtYg7JwyaAk7cc05lQDIR3s2TnazyYnzSzTIoA2ov3FOd+SUg/4RiDawrOhBzAaTbbsR6yRoCeNQGd1Vbdfxt5RBS6QZVy1hgnZfSz86jJ7uJ0d3WV/QnuqVBG2pFkMVKINWLfwaMzYRQd9I0OTVmINd/bMACVrCSIrUXWiq8mw6/lbDyC7KoCXsQFluBtbMKgIAvHLYYNuBTSq0oXAEjX3MqzlP0ylhJGiV4nU75V6NljRtDCNBSxCJwCsngVYglLzpvsq2FVrtPLRUoSXsgmm2A5tUaBv7RhAVRWR6nKhQ+3sdjWgSDUjEZ9GmZ2OYXaeEASRoCSPJLAIyCti2glnrsqC103AFUdTEQ0tNYYTd4PFdrx/rQHAiYtyBeYV2sBkIWzfij9sN5pXkqPcqDnexvGvBCWSXa7C69CXmo7XRdUZD7JpBC5CgJYxGhe1AthzYKeVAo+k91BRG2I2VNYUoy/FhOBjGW/Xdxh04qwTw5QEQgb7kb5yNQtOGMDnhoBJwONXvL42pLZSGKwymeYWWLAcEcRaKlUd38epkYHwCQ+MmzIlXwqBUnc0sVjy9RxRFshwQtsPhEHC1ZDt4xUjbgSDYwkd7qidWoVUNJRxoRo10nUnHCm1gbAL9oyEAwGyyHBDEWVBxocnxuZErDRWwjY9WA/9s30gQoTCbuFSRS4KWsA/cdvCy0Y1hNvDRxlsOVEMZtJrBK7Tp6KHldoPibC+yvfYb4EOCljAWlRcaebiCXZIONGwIK872wuumx4mEfbhiUQVcDgEnuofkiqQh8MxrhUNcjEAXywFVaFVTKzWFpWPKAR/0YUe7AUCCljCa+GlhCoKreWyVfSq0GgxVoMguwqbkZrhx8bxSAAZPDStewF4tajmYiETRKI1X1cZyIP2/zSVBq5YaqSms3T+GiUjU5NUYS0OvfSO7ABK0hNGonLVuu+guDRMOyD9L2BFTbAfxzacWnPjU2DeMSFREhtupze+1BsNbCEZZjg9upwNRUUS7nRJ1NECu0NrQPwuQoCWMRuWsddsNV6AMWiLN4Xm0b57qxkgwbMxBC+cAggMIBoARAxMWEiS+IUyT8aKDJGi1wuEQYlm0adYYZuehCgAJWsIM5OrJiaQ/ar8KrXpBK1sOKIOWsCELy3JRW5CJUDiKN091GXNQlxfIr2XbFmwMO9XDhIMm/tnQMDDWz7b5z0yoYpbUGNbUn16NYXbOoAVI0BJmUKQ8uovP2m61g6CNTABDHWybLAdEmiIIAjYuYVXaV492GHdgC/toT2oZ2cWrs748KX+XUEudJGgb+4dNXolxRKMiGqRRzFShJYhEKeYdyAosB/k2Gq4QaAMgAk4vC3tXSBsJWsLmcEG79YiBebQqhrjojaYZtIPN7JWqs5pRJwm6xr70qdC2+8cwPhGByyHIFWq7QYKWMB4108KkCm3X0DjCVu9AjbcbqPDJ8cYEshwQduVvFpTJ8V2new2K7ypSfuOsN8e7AwCABaVaCtpZ6vdFAIiv0KaPoOU3WXVF2XA57SkN7blqwt4UK5+1Xprtg8shICqK6AxYd047AE38s+FIFF1D7OekCi1hV/IyPFg3uxiAgbYDiw5XCE5E5MrfwrJc9Tv0S4JWha2JmEw6emhjgz7saTcASNASZpBVCnilWev9p5P6qMMhoEJOOrC4j1aDyK7OwDhEEXA6BJRkezVaGEEYj+E+Wu6hHWwCwkFjjpkA9b3DiIoicn1ulOX41O+QLAeaU1cUE7TRqPVi3/RAtsEUa/DUwCRI0BLGIwgxH62C6oncGGb1SS4aCNoWafxiVV4GnA76dSXsCxe02453IhSO6H/ArFLAmwuI0aRvnPUk3m6gTWQXCVqtqcrLhNMhYCISRUfABv0aGlAvDVXQxNdtEnSFJMxBTjpQLmhbrJ50oIHlgKc5VBeQf5awNyuqClCS7cVwMIx3T/fqf0BBsKSP9ngXE7Sa2A2A2I0zCVrNcDkdchZtY196JB1o2qhoEiRoCXNQMWu9VhpNyKuXloVXTlQIWj5PvCbfnl2nBMFxOARsWFwBAHj1qEFpBxb00cqCtlQDQTsxCoz0sG3y0GrKrIL08dGKokiCliAUo+JCw++cW6xsORDFWOWkQHn3sVyhpYQDIgWQ47uM8tGqyLzWC2450KRCyzNovbmAL1/9/ggZOborDQRt99A4hoNhOARBTniwIyRoCXNQMWtdHkto5QrtcBdLcBAcQK56y0ENWQ6IFOCKRaxCu791AB1GjK8uVj6VUC9OdLNKmCYV2nj/rBZ+XEJGju5KgyxaXp2tLcyE1+00eTXKIUFLmEPhHAACEPTHHpklSI1sObBwhXawib3mVgNOt+Ld8J+RKrREKlCa48OqmkIAwGtGVGnjvfpJ3jjrQd9wEH0jLHFBk0e7fmoI0wuedJAO08L4KGY7JxwAOgva/v5+3HTTTcjNzUV+fj5uvfVWDA/P/I/jsssugyAIk76++tWvTnpPc3MzrrnmGmRmZqK0tBTf+ta3EA6H9fxRCK1xZwD5kucryYYNXq3sCIxhwqrDFTTqPKYKLZFqbFxioI+W3ziP+4FRAxrRzgK3G9QUZCLL61K/w0HKoNWLdMqijWXQkqCdlptuugmHDx/G66+/jpdeegk7duzA7bffftbP3Xbbbejo6JC/fvSjH8l/FolEcM011yAUCuHdd9/F448/jsceewx33323nj8KoQcKJ4aVZvvgcTkgirEpWpZjoJG9qvDPhiNRtEuPZalCS6QK3Ef72rFORKI635C6M2I3lRZoDNO0IQygyC4dqStkHtp0yKJNhYYwQEdBe/ToUWzduhW/+MUvsHbtWlx00UX48Y9/jKeffhrt7TPfmWdmZqK8vFz+ys2N/fK/9tprOHLkCJ588kmsWLECV111Fe677z489NBDCIVCev04hB4ojO5yOIRYdJdVbQfccqBiHGVnYBxRUYTLIaAsV4MAdoKwAGvripHrc6NvJIi9zf36H9BCPlpNG8IAErQ6Ul2QCYcgIBiOTWtMVWKC1r5TwgAdBe3OnTuRn5+P1atXy99bv349HA4Hdu3aNeNnn3rqKRQXF+Pcc8/Fli1bMDoaEy07d+7E0qVLUVZWJn9vw4YNCAQCOHz48JT7CwaDCAQCk74IC1CsPLqrRhK0zVZ9HCQLWuUXGh5LVklDFYgUwu10YP3CcgAGTQ1TkXmtNbxCu6BUo0oYZdDqhtvpQHU+m0qZyrYDURRxkiq0M9PZ2YnS0tJJ33O5XCgsLERnZ+e0n/v85z+PJ598En/961+xZcsW/PrXv8bNN988ab/xYhaA/N/T7ff+++9HXl6e/FVTQ34jS8BHU6qJ7rLqcAW5cqJBZBf5Z4kUIxbfZYCPVkXmtdZomnAQHmdpKgCQR4JWD2bJSQep2xjWPxKCf2wCADCnKM0qtN/97nfPaNr66NexY8cUL+j222/Hhg0bsHTpUtx000144okn8Oyzz6K+vl7xPrds2QK/3y9/tbS0KN4XoSG8cjLQCESSs4vUWjnpIBqJTQnTIIOWhioQqQYfsPBeQx8GRoP6HkzFjbOWhCNRuflGmwxa6abZkwNkFKjfH3EGPIv2dAoLWl6drcrPQIZHg0ZFE0l69d/85jfxpS99acb3zJkzB+Xl5eju7p70/XA4jP7+fpSXlyd8vLVr1wIATp06hblz56K8vBy7d++e9J6uLnaXOt1+vV4vvF5vwsckDCKngp2MQ0Ns1nrJooQ/aunhCoE2IBoGHG4gO/F/6x+FIruIVKW2MAuLy3NxtDOAvxzrxGdWKr/xOyvFH7lxdnr0O9YMnO4dRigcRabHKd+Qq0JuPK2jDFqdmFvMBG1DCgvaE90aNyqaSNKCtqSkBCUlJWd937p16zA4OIi9e/di1apVAIA33ngD0WhUFqmJsH//fgBARUWFvN8f/OAH6O7uli0Nr7/+OnJzc7FkyZIkfxrCVASBXWza9wE9x5MUtOyCYMnhCrLdoAZwKA+ppsguIpXZuLgSRzsDePVoh76CNrsc8GQDoWGgvwEoWajfsWbgSKcfALCoLA8OhwYCVIMkFWJm5kiC9nRv6gvaBSkgaHXz0C5evBgbN27Ebbfdht27d+Odd97BnXfeiRtvvBGVlcw/1dbWhkWLFskV1/r6etx3333Yu3cvGhsb8cILL2Dz5s245JJLsGzZMgDAlVdeiSVLluALX/gCPvzwQ7z66qv43ve+h3/4h3+gKqwd4SK253hSH7N0hVaDhAOAKrREasNtB1uPtkPUc+iBIMT5aM2zHXBBu6RcI+HABW1+nTb7I85gThpYDrivW7NGRRPRtXX6qaeewqJFi/Dxj38cV199NS666CL8/Oc/l/98YmICx48fl1MMPB4P/vKXv+DKK6/EokWL8M1vfhOf+tSn8OKLL8qfcTqdeOmll+B0OrFu3TrcfPPN2Lx5M+699149fxRCL2R/mzJB2zcSxGjIYkM1BtQnHABUoSVSm0vmlcLndqJtcAyHO/z6HswCPtqYoM3TZof8xrmgTpv9EWcwR5qc1dw/ilA4YvJq9CEmaO1fodXVAVxYWIjf/OY30/55XV3dpDvzmpoavPnmm2fd76xZs/DKK69oskbCZBRWaPMzPMj2ujAcDKN1YBQLtMp11AINKrQ0VIFIdTI8Llw2vxRbj3Rg65F2nFuZr9/BLBDddbSTPdpdrJWgjffQErpQnutDhtuJsYkImgdGbR9r9VGiUTHOcmD/n43CLQlzKZEqJ30nWTpAggiCYN3oLu6hVeFto6EKRDqwcTGzn+meR8szr02q0EajIo5qWaEVRfLQGoAgCCnto23zj2JsIgKXQ5ATHewMCVrCXPLrAKeXZSryymaC8Dgryw1X0KBCy+0GNFSBSGW4j3ZHfTdGgjpah+IrtHr6dafh/2/vzuObqtL/gX9u0iTdm+4bXWlpy1J2KptUKLagDrjr4MKI6KjIMIqA358CLiMo7sq4sjkyIIrICIKyFWQRkL20lLa0FLrv+5Im9/fHzb0ldEuam7XP+/XqKyU5OTm5pMmTc5/znKuVDWhSqSF3kAgBklEaK7hFbgBtqmBiQh6ttuSaPeHTDSJ9XCGT2v7njO0/A2LbJNJeb01plTO0bS1ArbZYvBEBLb9LGG2qQOxZjL87wrxc0NqmQWpWiekeyDsKAAM0VQGN5aZ7nC5klHCzszF+7nAQI3DgvzS7BQEOdAbHlIQZWjtcGGZPJbsACmiJNeDL6JQZtiFHe6UDK5qhrc4HwAIyF8Cl5/J2XaFNFUhfwDAMUvhqB+km3DVM5tSea1qaYbrH6UK6dtFbnGgVDnK5S8qfNTk+oM0ps8eA1n4WhAEU0BJr4KMNaA2cobXK3cL4DxqvCKOKnVPJLtJX8NvgmjyP1i+OuzRwAaoYRK9wQPmzZtNfW+nAnmdo7WFBGEABLbEGRs7QWtXmCpXizJxQyS7SV0weEAAHCYOssjrklJkwT9Gnd+8zYjBZQEs1aE3uxkVhJq2XbAE0Q0uI2IQPmssGLdgIuWGG1mreaIRTgRFGdUMztKSvcHeSYVwkl55j0llavkSggTWvjcWyrPglu6gGrdmEe3GfM7XNKlQ2tFp4NOJpbVMLW/rSDC0hYvHuDzBSoLUOqNP/A42fvaxvaUNNk8pUozOMSLltNENL+hKz5NH6aQNaM+fQFtY0obZZBamEQbRYdUypBq3ZOMkdEOThBADIsaNKB1fK66HWsHCROyBQ+/xsHQW0xPKkcsArkvvdgNOBznIHeLtw2x1bzcIw/oPGq/cztG1qDYpqaVMF0nfwebT7L5eYbkcmnwEAGK7KQYP5Kh2kFVYDAKJ93aCQSY3vUK0Caq5zv1MOrVn0t8NatDduecsYsd7DmlBAS6yDb+8WhrXn0VrBwjBWc8PMSe8D2uLaZqg1tKkC6TuGBnvC380RDa1tOHKlzDQPInNur9lqxrSD89qAdohYO6HVXOPeaxwcAVd/cfok3Yq0w4Vhl/hScta0y6aRKKAl1qGXCzZClFZUuquumNsggpECHiG97oY2VSB9jUTCCJss7E43Qx6tGdMOLmgD2vhgpTgdCgvCQgGG3h/MQSjdZUcztKLndVsB+msg1sHIGVqrKN3F588qQwCprNfd0KYKpC8SAtoME+bR8gGtGUt3XRB7hrbyCnfJp2kRk4vSBrTZpqzCYWb8Zh9xNENLiMh6XbpLW+nAGnYLqxSnwgFtqkD6oqmxgWAY4HxBNQpN9fcsVDowT+kulVojlOwSL6Dla133F6c/0qNobVkrvm6rrWNZFpdKuOcSSwEtISLj91pvrDBowUaoNc7QUskuQgzm6+aIUaHeAExYvov/4lxqnoA2q7QOrW0auCocEO7lKk6nVfwMrXHvM0R/fHWK4tpm1DVbSUUdIxTXNqOmSQUJw9hNDVqAAlpiLeQuvVqwwc/QWsXmCjfuEmYEKtlF+iq+fJfJA9qGUqCx0jSPcYMLhVUAgMGBSkgkIq0kp5QDs1M6y+HrylXUsYe0gwztWYNIH1dxKm9YCQpoifUQFoYZEtByQd/16kZoNBbeXEHkXcJohpb0NXwe7W+XiqDWaMR/ALkr4GG+SgfnC6oBiLggjNXckHJAAa05RWs3H7CHtAM+oLWn/FmAAlpiTXqxMCxY6QyGAVrbNCirbzbRwPQkQskuoD3lgGZoSV+TEO4DDycZqhpbcfKqiWZQe5mv3xuiLwirLQDULYBEZlQlFWK4aF8u+MuyhxnaEvurcABQQEusSS9Kd8mkEgS6c7ucWDSPtrkGaNJ+ABsxQ0ubKpC+zEEqwdRYE+8aZsY82gtF1QBMUOHAMwyQOIjTJ9ELvz1sVqkdBLT8DG0AzdASYhpGb65gwTxaPn/WxRdQ9H57S9pUgfR1KaYu3+Ubx12auNJBbZMKeRXcexKV7LJ9/MKwy3YR0NIMLSGm5TOAu6wtAFr0z1MSSndZcoa2Ipu7NLKUDm2qQPq65DhuG9yTVytRUd8i/gOYqRZtmnZ2NljpBC/tFt1GE2ZoKaA1Nz6H1tZTDmqaWoWzgPZUsguggJZYEyfP9q0cy7P0vlv7bmGWDGi1HzTeUUZ1Q5sqkL6un6czBgV6QMOy2JtpgmoHvtovznVFQHO1+P1rnSvgKhyINjsLtAe03lSD1tyitDO0FQ0tqGwwwRctM+FnZ4M8nODhJLfwaMRFAS2xLr1YsCHsFlZtwZSDSu0MrZEfNLSpAiFAinaW1iTb4CrcAfd+3O8mnKU9c40LaEf08xKvU6pwYDGuChmCPLj1GrY8S9ueP2tf6QYABbTE2vgYvtd6qDbl4GqlBQNakVIO8mlTBUKQMrA9j5ZlTVCOzwyVDk5f4xaJjggRKaBlNTfk0NKmCpbA59Ha8sIwe9zylkcBLbEuftoFG2X6B7Th3hYOaFm2PaA1MuUgX/scwrxohpb0XRP7+8FZLkVxbTPOXq8S/wFMnEfb2qYWKhyIFtDWFQNtTQAjba+lS8yK31XLlmdo04tohpYQ8/AfxF0aMEPLB3/Ftc1oVqlNMaruNVVyZbsAo08FXqWAlhAoZFKhfNfOiwXiP4AwQ6v/+4wh0otr0NqmgdJJLnzhNtqNJbukMnH6JAaxh80V0rQBrai53VaCAlpiXfiZk9oCoEm/mRlvFwWc5dz2ffmWmKXlZ2fd+wEyJ6O6ulpFAS0hAHDHoGAAwI40UwS0/Jkg08zQnubzZ0M8wTC05a29sPWUg9omlTBpMjiIZmgJMS1HjxsWbOiX38YwDMK9XAFYKO2gIoe7NHJBWENLG8q1ZYoooCV93fRB3MKwE1crUKItMyQa396VCNSX6PmzQPsXZyrZZTEx2rzTzNJa0+R2m9jFG0rJeTqLVErOilBAS6yP/0DusuSi3nfhA8CrlthcQaz8We3Y3Rwd4OFEpxRJ3xasdMaIEC+wLLBL7F3DHJWAG5fSYIpZWpMGtHy9bmJ2Ub5ucJAwqG9pEyrS2BI+3WBwoNKyAzERCmiJ9eFPBxqQR8vnqeVV1JtiRN2rFKfCgZA/6+ki3mlKQmzYnYNNmXZgeEUVfag1GmEhm6gBLb+Doo9xX5xJ78mkEiGPlq/nakv4zT4ooCXEXPiFYQYs2BBmaC2SciDOpgpU4YAQXXxA+2tGEVrbRF7w6ac9E1SaLmq3mSV1aFKp4apwEHIujaZWtW+v7R0tTp+kV+L8udzTdG09V1uSVlgNwD7zZwEKaIk14kt3lVzkSmLpIVwbBOaZO6BlNUAln0NrXEBLFQ4I0TUyxAv+bo6ob2nDoexScTsXKqron9qkDz7dYFg/T0gkIp1pqb4KaNoAmQvgHiROn6RX4gK4PNoMGwxoL/ABLc3QEmImPgO4WovN1UB9sV53CbPUorC6IkDVyI1XaVxtSKpwQIguiYTBHaZKO+BnaIvT9P7irA8+oB0u5g5hfLqBd3+AoY9tS+Lrt9paQFta14yy+hYwDDAwkGZoCTEPB8f2igF6ng7kg8CC6iao1BpTjawjvsKBZ7jRtSH5YJzf+YwQ0p528HNagbgry31jueCwqRKoLxGt2+NXywEAo8PEDGizuEsfSjewtIEBfMqBbeXQ8ukG/X3c4Cx3sOxgTIQCWmKdhLQD/QJafzdHKBwk0LAsrleZcfVphfaDxsiSXQClHBDSmaSYAMgdJLhSXo/MEhGDCJlTe5qQSGkHrW1qnMrnZmhvCfcRpU8AN8zQUoUDS4vxdwfDABUNLSira7b0cPTWviDMPmdnAQpoibXy4/Pb9AtoJRJGmNk0a9pBGb/y2LgPmja1BgXVXK1NCmgJaefmKENilD8AE6YdGFAisDvnC6rR0qaBl7McUWItCANuKNlFFQ4szVnuILxHZ5TYTtpBWqG2ZJcd7hDGo4CWWCfhgyZN77sIpbsqzVi6q1xbw9InxqhuCmoaoWFZyB0kCHA3brcxQuyNycp3+Q/mLkUKaP/I49INEsJ9xC29J8zQUsqBNeArHdhS6S5+QdigAJqhJcS8ArQfNGWXuJI1erBI6S7+g4avadlL/JhDlM7irYwmxE7cMZhb2X/4ShmqGlvE61jkGVo+oL0lQsR0g8ZKLs8XMLqSChEHX+nAVkp3qTUaIaAd2s/TsoMxIQpoiXVShgFyN0Dd2p6n2gOzb3/bUsttnQm0b6XZS8KCMEo3IKSDSB83DAzwgFrD4tf0IvE65kt3lWfq/cW5O3/kagNaMfNn+fc/936AnN4frMFAG6t0kF1Wj4bWNjjJpBjgJ2IqjJWhgJZYJ0bS/mFTrF/aQZi5a9HyK49d/bmtNI1w4y5hhJCO7hrCpR1sv3BdvE6VoTd8cc42qqvy+mbklHPpTmPCvMUYnbZj2iHM2rSX7rKNlIOz17kZ/vhgJaQS+w377PeZEdvH57cVn9erudlTDvg94H2Ny58FqMIBIT2ZGR8CANh5sQAtKpF2DWMkuhu5GOF4XgUAINbfHUpnubEja8d/caYKB1aDTzm4Xt2I2ibjZ/ZNjd+KeViw/aYbABTQEmvG59HquTCMXxSWX9kAtcYMtWjLLnGXRi4IAyigJaQnY8K8EejuhLrmNuy/rN+GK3rhzwQZsAC1MzcuCBNVBdWgtTaezgoEK7nFu3w5LGt2tkAb0Npx/ixAAS2xZgHx3KWeHzRBHk6QO0jQpmFxvdoMtWiFU4HGB7R5FVxAywflhBBdEgmDmUP7AQC2nRMx7UAIaC8Y1U17/qyI6QYAUJrBXYpwJoiIJz6ICw7Pa4NFa3bmGjfG4SEibvZhhSigJdbLL447JdhQptdOPlKJRMhBzS03Q9oBX7LLyAVhGg0rbHsb4e1q7KgIsVt3a9MOtl+4Lt5ZmICh3GVx7wNalVqDY9qAdlykrxij0nbcCFTlcb/zFRmIVYgPVgIAzmurB1ir4tomlNQ1Q8IwGGLHNWgBCmiJNZM5A17aHbj0/LDhA8IrFSauRdvW3P5BY2TJrsKaJrS2aSCVMOindDZ+bITYqcQB/lA6yVFa14yjV8rF6dR/EACG+9Lcyy1wT+VXoqG1DV7OcgwOVIozLkCbp88Czt6Ai4iBMjFavDY4PF9QbdFx9ITPnx3gZ79b3vIooCXWzcA82kgfLqDNNXVAW5ENsBrA0QNw8TOqK36soZ4ucJDSnyQhXZFJJbhTW5N227lr4nQqd2nPTy3SbwHqzQ5mc4HwxCg/cetI8+kGNDtrdeK1C6wuFFaDZVkLj6ZrZ65xFQ6G23n+LEABLbF2QqUDfWdotSkHpg5oy27YIczIHYH4sUZQ/iwhPbpnWCgA4Iez+eIFEny+fvG5Xt39YFYpACAx2l+c8fDK+PxZ484CEfHF+LtDJpWgtlmF/CozbuZjoL6yIAyggJZYO/8h3KXeM7Rc0egr5WYKaEVYqNEe0FL+LCE9SYkLhKvCAdeqGoVSWUYTAlrDZ2jb1BocvsIFtJOijDtb0wFfSYUvLUashkwqETZYsOa0A6FkVz/7XhAGUEBLrF2g9oOm/DLQ2vO3YPPN0GpnTkSocJBbQQvCCNGXk9wBM+K5agffnb4qTqf8+0wvUg7OXq9CXXMbPJxkwkIh0QgVDiigtUbCwjArDWjrmlXIKqsDQDO0hFieWyC3Exer0WuWlg8Ki2ub0djaZrpxlaZzl3zJHyNQygEhhnlgeBgA4PszV6HRiJB2wFc6qMoFmg3bzvRgNjc7O7G/n7i7MLXUAjXaPGE/SjmwRsLCsELrLN11Kr8SLAuEejrDz83R0sMxOQpoifULHMZdFp7tsamnsxweTjIA7bVdRadqBCpyuN/5HF8jUMoBIYZJjguEu6MMBdVNOJpbZnyHzl6AOzfra+gGCwezuAVh4qcbaNOaXAMAJ/s/XWyL+IVh1jpDezKfS8kZLeZWzFaMAlpi/fiAtuhsj00ZhhECQ5OlHZRmAGC5Mjquxn2ItbaphU0gInwooCVEHwqZFDO1aQdbTueL02kv0g7UGg1+z+EC6kliLwgTKhxQuoG14lMOLpfWocmUZwR76cRVCmgJsS5Bw7hLPQJaAIjka9GaamEYv+e7COkG+VWNYFnASSaFfx84JUSIWB4Y0Z520KYWYZOFXiwMO5FXgeqmViid5OKXRaL8Wavn7+YIX1cFNCyLi0WGpaqYw0ltQDuGAlpCrAQ/Q1t2iTvd3wOTz9DyAa2fCOkG2qA73NsFjJHlvwjpS6bGBsDbRYHi2mbsyyw2vkM+oC3Sv3TXrvRCAMDtcQHi15DmF55S/qzVYhhGWGx15rp15dGW1jXjamUDGAYYGUIBLSHWwS2Q27yA1QDF+iwMM3Glg1LxZmgpf5aQ3pE7SPHQSG6W9j8nc43vMGg4d1l2Sa+KKkB7QDttYJDxj38zmqG1CaNCuWDxz3yRSsiJhJ+djfFzh7t2XYm9o4CWWD+GMSjtgN8tzCTb37KsqCkHeZVUsouQ3npsTAQA4Mez11DXrDKuM/cgbgEWq9Yr7aCktgl/5nO7MKWIHdDWlwL1xQAYwJ92CbNmo0K5BXv8a8Fa9LV0A4ACWmIrDFgYdmPKgehbEtYXA02VACOlTRUIsbDRYd6I8XdHk0qNH8+KsBVu8EjusuB0j01/zSgCwG0pGuDuZPxj34gPqL2jADm9N1gzfob2QmE1mlVqC4+mXV9bEAZQQEtshRDQ9pzfFq4NDuua21DR0CLuOPjZWZ9owMH4RVxUg5aQ3mMYBo+O5mZpvzlxxfgO+bSDwp4DWj7dYPogE6Qb8AFtwBDx+yaiCvF0hq+rAiq1BucLrCOPlmVZoWQXzdASYm34lIPSDEDV1G1TR5kUwUpuxiRH7EoHfI1KEdINANoljBBjPTImHABwIKsEVyuN/HsPHsFdFpzqtplao8Fv2hlak+TPCgFtvPh9E1ExDHNDHq11pB3kVTSgvL4FMqkEQ4Ptf4cwHgW0xDa4BWl3DNMvvy3Kxw0AkKPd9k80QoUD4wPaumYVSuuaAbTn/RJCDBPm5YopMQFgWeDroznGdcbP0FblcqlFXTiWW47KRq5cV0K4j3GP2Rm+Fm4gBbS2oD2P1joWhh3TbjYyNFgJhUxq4dGYDwW0xDYwDNBvNPf79ZM9No/y5QLabCueoc0p54JtX1cFPJzkRvdHSF/19PgoAMCaozlQGVOT1skL8Irkfu8mj/a7U1cBAH8ZEix+ua6WOqBSmz7Bb8lLrJq1zdAeucIFtBP6i7x7nZWjgJbYjuBR3KUhAa2YM7SqRq6kDwAEGv9Bk13GBdv8WAkhvTMjvh/83BxRVNuEHWkFxnUWpE07KDzT6c1qjQY/nOV2J3tQWzZMVCUXAbDcWSkXE8z+EtHxM7QXi2rQaAU7hh3mA9pIXwuPxLxMGtBWVlZi1qxZcHd3h1KpxJw5c1Bf3/WMWV5eHhiG6fTn+++/F9p1dvvmzZtN+VSINRBmaP/ssalJAtqi81wtXLdA7sdI/Nj6U7oBIUaRO0jxxC3czOrnv2cZ1xlf6aCLhWGHsktRXNsMT2c5kmICjHusztCCMJsTpHRGoLsTNCyLsxbeYKG6sRUXCqsBAOP7U0ArmlmzZuHixYvYs2cPduzYgUOHDuGpp57qsn1ISAiKiop0fl577TW4urpi2rRpOm3XrVun027mzJmmfCrEGgQNBxgJUHsdqC3stmmULxckZokZ0PIzNnyenZH4lAOaoSXEeHPHR4FhgN8uFRmXOx90w8KwTsr+8ekGdw8NgdzBBPmJtCDMJvGztHz9V0s5llsGluU+V0QvJ2flTBbQZmRkYPfu3fj666+RkJCACRMm4JNPPsHmzZtRWNh5MCKVShEQEKDzs23bNjzwwANwddWdxVIqlTrtHB2NL6FErJzcpT13tYe0g/7aRWHl9S2obmwV5/GLtAFtoDgBLaUcECKeSB83JMdxZ04+PpjZ+44C4wGJA1BfAtTo1rZtU2uw9Rx33YMjTJBuALQHtLQgzKbwiwOPahdkWcrhHO7xx/exdAPAhAHtsWPHoFQqMWrUKOG6pKQkSCQSHD9+XK8+Tp06hbNnz2LOnDkdbnvuuefg4+ODMWPGYO3atd0W0G9paUFtba3OD7FRwfotDHNzlMHfjfuSw8+EGk3kGdr2lAMKaAkRwwu3cdvEfn00GxX1vaxBLXNunx29pvtZtf9yCcrrW+DjqsDkAf7GDLVzalX7lrc0Q2tTJmhP7x/OKRN/Qx8DtC8Io4BWNMXFxfDz011h5+DgAC8vLxQXF+vVx5o1axAXF4dx48bpXP/6669jy5Yt2LNnD+699148++yz+OSTT7rsZ8WKFfDw8BB+QkJCDH9CxDrwebQFZs6jbakFyrW5eSIEtE2tbbhe3QigPT2CEGKcpNgADOvnicZWNT47fLn3HYXcwl3mH9O5+quj2QCA+4eHil/dAABKLwLqVsDRA1CaaAaYmMToMG/IpBIU1jQhT1tf3Nxa29Q4rk156GsLwoBeBLRLlizpcuEW/3Pp0iWjB9bU1IT//ve/nc7Ovvrqqxg/fjyGDx+OxYsXY9GiRVi1alWXfb388suoqakRfq5dE2GLRGIZfEBbeIabzehGe0ArQumuonMAWMAjVJSVx/yGCh5OMni7KIzujxDCLRh+aQo3S/vJwcto6u2K81BtQHvDDG1BdSO2adMNnpkQbdQ4u8SfeQoexZUqJDbDWe6AkSFcHu3hK6UWGcPpa1VoVqnh7aJAjL+7RcZgSQYHtC+++CIyMjK6/YmMjERAQABKS3X/U9va2lBZWYmAgJ5Xhv7www9obGzEY4891mPbhIQEXL9+HS0tnZ9iUigUcHd31/khNsq7P+CoBNqagZIL3TblZz5FmaEV0g2GGd8X2scU5eMGhj64CBHN/SPCEOrpjNK6Zqw/3svtcPmAtuQi0FwDAPjySDbUGhYT+/tiiKl2X+IruPBf3IlN4U/zH8mxTB7toewSAFz+bF/8XDE4oPX19UVsbGy3P3K5HGPHjkV1dTVOnWrfQnD//v3QaDRISEjo8XHWrFmDv/zlL/D17Xna/OzZs/D09IRCQTNddo+RtL/Z53efi83vFpYtRg6tqfJnKd2AEFHJpBIsnDIQAPDGrrTe1QV19Qc8wwGwwPWTaG1T48sjXMrRc7cOEG+wN+NnaCmgtUlCHu0VywS0+zK5gNYk+d02wGQ5tHFxcUhJScHcuXNx4sQJHDlyBPPmzcNDDz2EoCBu7+uCggLExsbixIkTOvfNzs7GoUOH8OSTT3bo9+eff8bXX3+NtLQ0ZGdn47PPPsNbb72F559/3lRPhVibsPHc5dXD3TYTNYdW7IC2vH2GlhAirqfGRyHc2wVFtU346EAvU+CEPNo/sO3cNRTXNiPA3RF3DzXRGozGSqBSu3Vv8AjTPAYxqXERXEB7sagGlQ29XJTYS80qNQ7lcGfFk2JNUB/ZBpi0Du3GjRsRGxuLKVOmYPr06ZgwYQK+/PJL4XaVSoXMzEw0Njbq3G/t2rXo168fbr/99g59ymQyrF69GmPHjsWwYcPwxRdf4P3338eyZctM+VSINQmfwF1ePcJtdNAFfvazuLYZ9S3d59t2q7GC29sdEGWHMOCGlAMq2UWI6BQyKd68k/tbXbknHeX1zYZ3ok07YK/9gbd+uwgAeGp8tGlqzwLtC129o7kteInN8XVzFHJXj5p5lvbolTI0q9QIdHfCwAAPsz62tTBpQOvl5YX//ve/qKurQ01NDdauXatTTzY8PBwsyyIxMVHnfm+99Rby8/MhkXQcXkpKCs6cOYO6ujrU19fj7NmzePrppzttS+xU4DBA5gI0VbWXuOmEp7NCWHCVY8zCsPw/uEvfWNE+aHLKufFQygEhpvHwyHAM6+eJ2mYV3tidZngH2hnatmsnkV5QAQ8nGeZPihF5lDegdAO7wFcXMHfawd5MrnpUUmxAn8yfBUwc0BJiElIZEDKG+/3qkW6birJjWP5R7pI/BWmk1ja1UNaFUg4IMQ2JhMGqmVyK0CcHMw2fMfONAevoAZm6GcMdruHl2wfB29WE6zSEgHZU9+2IVZvYnytXeuByiVkfVwhoTbEds42ggJbYJj3zaAf4cad/MkuM2EyDr0UZNq77dnrKq2iAhmXhJJMi0KNvbU1IiDklxQZidkIkWBaY/Z9jhi0QYyTIcebSFu52v2La2VlWw221C9AMrY2bog0o/8yvNFsebVVjC/7Mr9B5/L6IAlpim8IncpdXj3a63zovVpvPlFnay4C2tUFbgxZA6Nje9XETfiwD/Nz77KkhQszlg3tHIljphKyyOiz+6Yze97tYVI3PCritdB/3zYeT3MFUQwTKL3Obt8icAb+BpnscYnL9PJ0xKNADGpbFvkz9NpEy1oHLJWBZYGCAB4KVzmZ5TGtEAS2xTcEjAAcnoKGM+zDoQox2hvZSb2doC/4ENG2AezDgIc7qZn62OMaf0g0IMTWlsxxr/sqlC3166DI+PZjZ433qmlW49+vf8UsTV6IrsOYcoGoy3SCvatOagkYAEhMGzsQsbo/lvgj9dqnILI+351J7/mxfRgEtsU1SORCiPTWX93uXzWID2lMOerW/9lVtukHoWNF27uFnaGP9++ZKVELMLXlgEJZPHwIAeP77P7Hpz7wu26rUGjyx8Q9kltSizi0CapcAMOoWnV3DRJd3iLuMuNV0j0HM5vY4bUCbUdS7zx0DaDQsfk67DgBI0T5uX0UBLbFd4do3/ysHumwS5eMGCcOgtlmF4tpelO65dkNAKxJ+tjjGj2ZoCTGXpdOGYJ52U4RHNhzFku1n0KxS67QpqmnCbR/txQ9n8uEgYfDd3yZCGnUbd2PuQdMMjNUAudov5RTQ2oVbo/ygcJAgv6rRuPUbejh1rRIF1U1wVThg8gCaoSXENkVN4S6vpALq1k6bKGRSRHi7AOjFwjBNG3BNu/JYxIC2PeWAtmAmxFwYhsFH943C3HFR0LAs3t6TjiFv7cSLP57CF4ez8MS3xzD4Xztw5EoZ3B1l2PrkrRjf3w+ImMR1cCXVNAMrTQcay7n82eCRpnkMYlbOcgeh2oGp0w62n78GAEgZGASFzEQ1km0EBbTEdgUOA1x8gdb69lqxneAXhl0qqTGs/6JzgKoBcFQCfnG9H+cNKhtaUFbPrXylgJYQ85JIGHz51wT89NStCHB3RHZZHd7ffwl/33wC6/64gsrGVgwO9MDJl1Lwl/h+3J34gLbwDFf7Wmy52nSDsHFcKhWxCzemHZjST+e5dIOZ/Ou1D6OAltguRgJEJXG/Z+/pslmMUOnAwFq02fu4y/AJ3GOJgJ+dDVY6wVUhE6VPQohhZsSHIOOVu7DukVsw79YBSIoJwAuTY/Hbc5NxavE0DLjxy6Z7EOATA4DtNl+/1/iANpzSDexJsjag3X+5xLidKruRXVaHi0U1kEoYTB8UZJLHsCUU0BLbJgS0e7tswi++MniGNvs37WNM7c3IOsUvCOOrLxBCLEPpLMfsW/rjkwdGY8/zU/DePSMxNS6w861t+0/mLi//Ku4gNG3tm8NQ/qxdGRKkRKSPK5pUavxysdAkj7FdOzubGO0PT2cTbvphIyigJbat/2Ru9rQ0Hai53mkTvjxWZokBM7SNlcB17d7q0SIGtJQ/S4jtiZnGXV7eBWjU3bc1ROFZrv6sowcQEC9ev8TiGIbB/cNDAQDfn8k3yWP8pM2fnTGE0g0ACmiJrXPyAoK1W0V2kXbAz9DmVdajSd+dgnL2A2C5IufuwSIMlMNXOIilgJYQ2xE6jsulb6wQt3wXX6ElfCIg6dsLeuwRH9D+crEADS0G7FKnh9zyehzOKQPDADOHUkALUEBL7AGfEpD1W6c3+7oqoHSSg2W5nCO98MExn9IgEko5IMQGSWXAgBTu90s7xes38xfusv8U8fokVmNEiBcivF3R2KrGrnRx0w7+czIXADB5QABCPF1E7dtWUUBLbF/sdO4yex/Q3DFPlmGYGyod6FG6i9W05+RG3y7WKNGm1iC7rB4ApRwQYnNi7+AuM3d2u9223mquA4WnATDtfRO7opt2cFW0flmWxYbjVwAAjydEiNavraOAltg+v0HcKmR1S5ezJwYFtEVnubqQcjcg5BbRhplbUQ+VWgNHmRSh9I2aENvSfwrg4AhU5QJlGcb3x79XhSQArv7G90esEh/Q7kgTL+3gcE4ZrpTXw1XhgHuGhorSpz2ggJbYPoYBBt/L/X5xa6dN+C1wM4r1qHRwSXsaMHISd6pRJPyCsAF+bpBIxNlGlxBiJnIXIFK7a1jGDuP7u/Qzdxl3l/F9Eas1MtQLUb5uaGxVY9OpPFH65Gdn7x8eCheFgyh92gMKaIl94APanAPcwo2bbw5UAgDSinoIaFkWuPAd9/vAu0UcIJCuDaZpQRghNir2Tu7ywhbj0g4aK9rLdfF9ErvEMAz+PiEaALD60GWwRqar1DWrsEWbvvB4QqTR47MnFNAS++AdBQQOBVg1kL69w82Dg/hatLVQqTVd93PtOFCdD8hd23NzRcIH00OClKL2Swgxk4EzAJkLUJFlXLWDzF1crn7AEMAzXLThEes0OyESjjIpzl6vwh+55Ub19dWRbNQ1t2GAn5uwvS7hUEBL7Mcg7SxtWse0g1BPF7g5OkCl1uByaTd5tOc3c5dxf+H2VhfRhcJqABTQEmKzFG7AIO2ZmzP/6X0/6du4y9i/GD8mYvW8XRV4aEQYAODfv1/udT+tbWq8f4DL316UNJBS125CAS2xH4PvAcAAVw8DZZk6NzEM0552oA0sO2hrAS5qP2jiHxR1aG1qjZC/y4+DEGKDhj/KXV7cBrQYuJ02AFTltW+rzadKEbv37K0DAABbzuSjtK65V31sPJmHguomBHk44ZHRVN3gZhTQEvvhEdJe/ub4Zx1u7jGPNus3oLkacAvkCp2LKKe8Hi1tGjjJpIjwdhW1b0KIGYUkAN7RgKoBuPij4ff/cw0Altvl0Lu/6MMj1ml0mDdGh3mjtU2Dt/dcNPj+Gg2Lt/emAwD+eVssFDLaiONmFNAS+3LLs9zluc3c9rU34PNoL3Q1Q3t6g7bhfaLv2sPPCg8K9KDTRITYMoYBhj/C/X5qnWGLw1RN7akKo+eKPzZi1V6/g9ve+JODl3Gl3LDZ/W9P5iKzpBYeTjI8NT7aFMOzeRTQEvsSOo7bE72tCTi1VuemblMOCk9zu4MxUmDU30QfVlpRtc4YCCE2bNgswMEJKDzT5Zbbnbq4DWiq4s4mRSebbnzEKiXHBWJqbABUag3+73/n9L5fRX0LXtx2GgCwZOoguDuJV07SnlBAS+wLwwC3PMf9fuIrLi9Wa7B2MdaVivqOBa4PreIu4x8AvMQ/DcinOQymBWGE2D4XX2CMdoZ1/xv6zdKyLHDiC+73kX8T/SwQsX4Mw2DVzBFgGOC701dx9EqZXvdbtP0MyutbMDjQAy9OiTPxKG0XBbTE/gy+B3ALAuqLgSMfClf7uTnC11UBlr1pg4Wic9ye6owEmPCiSYZEFQ4IsTPjF3Dl/YrPt2+S0J3z33G7EMqcgRGPmXp0xEoN7eeJ2dr6sQ+vO9zjArFf0wux9lgOAOCLhxMgk1LY1hU6MsT+SOXA7W9yv//+LlCRLdzEB5R8CgBYFkh9i/t98L2Aj/i5Sc0qNbJKuXypwYEeovdPCLEAZ+/2nP0D/9I5G9RBczWw5xXu91tf4mZ4SZ/13j0jEO3rhvyqRtzz1SG0tqk7bXfsShnu/fp3AMDfJ0RjXCS9brpDAS2xT4PuAaKSAHUrsGOBcEqQP+UvLAw78SVweTeXOztxoUmGcqmkBhqWhaezHIEeTiZ5DEKIBYx9DnDyBMouAbsXd93uwAqgoYyrjjB2nvnGR6ySp7MC/3t6EtwdZThypQwzvzyEgupGnTapl0sw7bMDaGhtw+2xgfjw3pEWGq3toICW2CeGAaa/xy3cyPsd+O3/AaxGWJR1obCa2+nnt//j2t/+JuAba5KhpBXy9Wc9wDBU4YAQu+GoBO7+CgDDVTw4tb5jmzPfAie/5H6fvoo7g0T6vNgAD2z+23jIpBLsSi/EwDd34B8//Ik3d1/AlI/34raP96KmSYWJ/X2x7albqUyXHiigJfbLMxxIWcH9/sdqYNvTGOHVBinUGFL0E9jNDwOaNm42N+EZkw2Dnw2mBWGE2KHoqcBkbTrBLwu5RWIttdzP4feB/z3HbXM7ag4QeZtlx0qsyrRBwTi1KAUJ4d6obVbh49RMvLrjPPZfLoFMKsFT46Ow4++3wVnuYOmh2gSGZQ0pomcfamtr4eHhgZqaGri7u1t6OMTUzm3mPlQ0XGWDBlYOF6aVuy1gCPC33dziDhOZ+sk+7M0sxhcPjcFTE6h+ICF2h2WBbXOBC99z/3ZwBNpuWOwzbj6Q9Dp35oiQm6g1Gmz68yrOF1ahsqEVXi5yzLs1BqFeLpYemsUZEq9R2E/s39CHABcf4LdXgLJLcGFaUa5xQdGw5zHkLy+a9BQgy7I4fY3b4GFEiJfJHocQYkEMw6UeDJwJ7F3WvhDVLQgY/w8g4e8WHR6xblKJBI+MiQBA29kagwJa0jdEJXE/rQ1Y+s1PeP9sM16Uj8YQE+ezXa1sQGVjK2RSCZXsIsSeMQwQeye3YUJ5JuAeBDjRl1hCzIVyaEnfIneBX/QoNMARp/IrTP5wp/K52dnBgR6U1E9IXyCVAf6DKZglxMwooCV9Dn/q/5Q2FcCU+HSDkaH04UYIIYSYCgW0pM8ZGuwJCcOguLYZRTVNJn0sPmgeSfmzhBBCiMlQQEv6HBeFA2L9udWSpkw7YFlWCGhpQRghhBBiOhTQkj6JTwE4fb3KZI9xraoR5fUtcJAwiA/2NNnjEEIIIX0dBbSkT+JTAPhFW6bA588OClTCkRaEEUIIISZDAS3pk9oXhpku5eAULQgjhBBCzIICWtInDQ/hFoYVVDfhelWjSR6Dn/2lBWGEEEKIaVFAS/okV4UMw/pxea1HrpSK3r9Gw+KPvHIAwOgwb9H7J4QQQkg7CmhJnzWhvy8A4MiVctH7Ti+uQVVjK1zkDhjejxaEEUIIIaZEW9+SPmt8pC8+Ts3E4RzxZ2j5Pm+J8IGDlL43EmLrNBoNWltbLT0MQuyKTCaDVCrOomkKaEmfNT6Sm6E9V1CNumYV3BxlovV9+EoZAGCC9jEIIbartbUVubm50Gg0lh4KIXZHqVQiICAADMMY1Q8FtKTPClY6I9zbBXkVDfgjtxxT4wJF6/twjjag7U8BLSG2jGVZFBUVQSqVIiQkBBIJnXEhRAwsy6KxsRGlpdwZzcBA4z6DKaAlfdqESF/kVTTg8JUy0QLaa1UNuFrZAKmEwS0RPqL0SQixjLa2NjQ2NiIoKAjOzs6WHg4hdsXJyQkAUFpaCj8/P6PSD+irJunTJvT3AwAc0aYIiIGfnR3ezxOuCvHSGAgh5qdWqwEAcrncwiMhxD7xXxRVKpVR/VBAS/o0Po/2j9xyqNTi5MfxC8L4YJkQYvuMze8jhHROrL8tCmhJnzYwwAOeznI0tLYJW9UaixaEEUIIIeZFAS3p0yQSBpMH+AMAdqcXGt1faV0zLhRWAwDG04IwQgghxCwooCV93vRBwQCAXy4aH9DuTi8Ey3L5swHuTkb3Rwgh9iwxMRELFiyw9DBMbvny5Rg2bJjZHm/9+vVQKpVG95OamgqGYVBdXW10X6ZGAS3p81K01Q1O5legrK7ZqL52XiwAANwxONjocRFCSG/Nnj0bDMNg5cqVOtf/9NNPNpUPvH79ejAMg5SUFJ3rq6urwTAMUlNT9e5r9uzZmDlzprgDtCOdfbkYN24cioqK4OHhYZlBGYACWtLnBSmdMayfJ1gW+DWjqNf9qNQa4f7TBwaJNTxCCOkVR0dHvP3226iqqjL7Yxu7Yv1GDg4O2Lt3Lw4cOCBan+bCsiza2tosPYxek8vlomx6YA4U0BICYJo2AN1lRB7tsdwy1DSp4O2iwJhwb7GGRgixIizLoqGlzSI/LMsaNNakpCQEBARgxYoV3bY7fPgwJk6cCCcnJ4SEhGD+/PloaGgQbmcYBj/99JPOfZRKJdavXw8AyMvLA8Mw+O677zBp0iQ4Ojpi48aNqKiowMMPP4zg4GA4OztjyJAh2LRpk0HPAQBcXFzwxBNPYMmSJd22u3btGh544AEolUp4eXlhxowZyMvLA8Cd8t+wYQO2b98OhmGE2d377rsP8+bNE/pYsGABGIbBpUuXAHC7xLm4uGDv3r0AgJaWFsyfPx9+fn5wdHTEhAkTcPLkSeH+/Cn6Xbt2YeTIkVAoFDh8+HCHsebk5CAyMhLz5s3r9P+VZVksX74coaGhUCgUCAoKwvz584Xbq6qq8Nhjj8HT0xPOzs6YNm0asrKyujw2nc1OL1iwAImJicLtBw8exEcffSQcn7y8vE5TDrZu3YpBgwZBoVAgPDwc7733nk6/4eHheOutt/DEE0/Azc0NoaGh+PLLL7scm1hoYwVCAEwfFIQVv13E7vRCqDUaSHuxG9DONC4YThkY2Kv7E0KsX2OrGq4vfmeRx65/70G4KPT/2JZKpXjrrbfw17/+FfPnz0e/fv06tMnJyUFKSgrefPNNrF27FmVlZZg3bx7mzZuHdevWGTS+JUuW4L333sPw4cPh6OiI5uZmjBw5EosXL4a7uzt27tyJRx99FP3798eYMWMM6nv58uWIiorCDz/8gPvuu6/D7SqVCsnJyRg7dix+//13ODg44M0330RKSgrOnz+PhQsXIiMjA7W1tcLz8vLywoULF/DFF18I/Rw8eBA+Pj5ITU1FbGwsTp48CZVKhXHjxgEAFi1ahK1bt2LDhg0ICwvDO++8g+TkZGRnZ8PLy0vnWLz77ruIjIyEp6enTmrE+fPnkZycjDlz5uDNN9/s9Plu3boVH3zwATZv3oxBgwahuLgY586dE26fPXs2srKy8L///Q/u7u5YvHgxpk+fjvT0dMhkhtc//+ijj3D58mUMHjwYr7/+OgDA19dX+ELAO3XqFB544AEsX74cDz74II4ePYpnn30W3t7emD17ttDuvffewxtvvIH/+7//ww8//IBnnnkGkyZNQkxMjMFj0xd96hIC4JZwHyid5KhsbMXJq70r3yXkzw6i/FlCiHW4++67MWzYMCxbtqzT21esWIFZs2ZhwYIFiI6Oxrhx4/Dxxx/jm2++QXOzYWsKFixYgHvuuQcREREIDAxEcHAwFi5ciGHDhiEyMhLPP/88UlJSsGXLFoOfR1BQEP7xj3/g//2//9fpKfzvvvsOGo0GX3/9NYYMGYK4uDisW7cO+fn5SE1NhaurK5ycnKBQKBAQEICAgADI5XIkJiYiPT0dZWVlqKqqQnp6Ov7xj38IAWhqaipGjx4NZ2dnNDQ04LPPPsOqVaswbdo0DBw4EF999RWcnJywZs0anfG8/vrrmDp1Kvr3768T6B49ehSJiYlYuHBhl8EsAOTn5yMgIABJSUkIDQ3FmDFjMHfuXAAQAtmvv/4aEydOxNChQ7Fx40YUFBR0mEnXl4eHB+RyOZydnYXj09muXe+//z6mTJmCV199FQMGDMDs2bMxb948rFq1Sqfd9OnT8eyzzyIqKgqLFy+Gj4+PyVNGaIaWEAAOUgmS4wLx3emr+P7MVYO3rM0tr8fFohpIGAbJIm2hSwixPs5yKerfe9Bij90bb7/9NiZPnoyFCxd2uO3cuXM4f/48Nm7cKFzHsiw0Gg1yc3MRFxen9+OMGjVK599qtRpvvfUWtmzZgoKCArS2tqKlpaXXWwgvXrwYX3zxBdauXYsHHnigw/PIzs6Gm5ubzvXNzc3Iycnpss/BgwfDy8sLBw8ehFwux/Dhw3HnnXdi9erVALgZW/60fE5ODlQqFcaPHy/cXyaTYcyYMcjIyNDp9+ZjAXBB6tSpU/Gvf/2rx8oO999/Pz788ENERkYiJSUF06dPx1133QUHBwdkZGTAwcEBCQkJQntvb2/ExMR0GIfYMjIyMGPGDJ3rxo8fjw8//BBqtVoIguPj44XbGYZBQEAASktLTTo2CmgJ0XpkdDi+O30V357Mw8oZwyGT6n8CY8PxKwCAxGg/eLkoTDVEQoiFMQxj0Gl/a3DrrbciOTkZL7/8ss5pYQCor6/H008/rZOfyQsNDQXAPeeb8zw7W/Tl4uKi8+9Vq1bho48+wocffoghQ4bAxcUFCxYsQGtra6+eh1KpxMsvv4zXXnsNd955Z4fnMXLkSJ3AnOfr23VNcIZhcOuttyI1NRUKhQKJiYmIj49HS0sL0tLScPTo0U6/CPTk5mPBjyMoKAibNm3CE088AXd39y7vHxISgszMTOzduxd79uzBs88+i1WrVuHgwYMGjwUAJBKJXv+HYrk57YFhGGg04uzG2RVKOSBEK2VgEPzdHFFa14xdBtSkVWs0WPcHNwPwxNj+phoeIYT02sqVK/Hzzz/j2LFjOtePGDEC6enpiIqK6vAjl8sBcIFYUVF7BZisrCw0Njb2+JhHjhzBjBkz8Mgjj2Do0KGIjIzE5cuXjXoezz//PCQSCT766KMOzyMrKwt+fn4dngdfckoul0OtVnfoc9KkSUhNTUVqaioSExMhkUhw6623YtWqVWhpaRFmZPv37w+5XI4jR44I91WpVDh58iQGDhzY49idnJywY8cOODo6Ijk5GXV1dT22v+uuu/Dxxx8jNTUVx44dw4ULFxAXF4e2tjYcP35caFtRUYHMzMwux3Hz/yEAnD17VuffXR2fG8XFxek8f4D7fx4wYECnKQrmRAEtIVoOUgkeHRMBAEKAqo99mSXIr2qE0kmOe4aGmGp4hBDSa0OGDMGsWbPw8ccf61y/ePFiHD16FPPmzcPZs2eRlZWF7du366z8nzx5Mj799FOcOXMGf/75J/7+97/rtfAoOjoae/bswdGjR5GRkYGnn34aJSUlRj0PR0dHvPbaax2ex6xZs+Dj44MZM2bg999/R25uLlJTUzF//nxcv34dALf6/vz588jMzER5ebkwQ8nn0V68eBETJkwQrtu4cSNGjRolzLa6uLjgmWeewUsvvYTdu3cjPT0dc+fORWNjI+bMmaPX+F1cXLBz5044ODhg2rRpqK+v77Td+vXrsWbNGqSlpeHKlSv49ttv4eTkhLCwMERHR2PGjBmYO3cuDh8+jHPnzuGRRx5BcHBwh3QA3uTJk/Hnn3/im2++QVZWFpYtW4a0tDSdNuHh4Th+/Djy8vJQXl7e6Yzqiy++iH379uGNN97A5cuXsWHDBnz66ae9msUWGwW0hNxg9i2RAIAdaQV6b7Kw5lg2AGDW6HA4yW3rVCQhpO94/fXXOwQp8fHxOHjwIC5fvoyJEydi+PDhWLp0KYKC2mtpv/feewgJCcHEiRPx17/+FQsXLtQrD/aVV17BiBEjkJycjMTERAQEBIiyscHjjz+OyMhIneucnZ1x6NAhhIaG4p577kFcXBzmzJmD5uZm4dT+3LlzERMTg1GjRsHX11eYaRwyZAiUSiWGDRsGV1dXAFxAq1arhfxZ3sqVK3Hvvffi0UcfxYgRI5CdnY1ff/0Vnp6eeo/f1dUVu3btAsuyuOOOO3RKpPGUSiW++uorjB8/HvHx8di7dy9+/vlneHtzJSHXrVuHkSNH4s4778TYsWPBsix++eWXLr9oJCcn49VXX8WiRYswevRo1NXV4bHHHtNps3DhQkilUgwcOBC+vr7Iz8/v0M+IESOwZcsWbN68GYMHD8bSpUvx+uuvd0hlsQSGNbSwnZ7+9a9/YefOnTh79izkcrle26axLItly5bhq6++QnV1NcaPH4/PPvsM0dHRQpvKyko8//zz+PnnnyGRSHDvvffio48+El6E+qitrYWHhwdqamq6zWEhfdOYVbtx8moF3rt7BF6Y0v2CiIr6FgS98iNa2zQ4vXgahod4ddueEGJbmpubkZubi4iICDg6Olp6OITYne7+xgyJ10w2Q9va2or7778fzzzzjN73eeedd/Dxxx/j888/x/Hjx+Hi4oLk5GSd0iGzZs3CxYsXsWfPHuzYsQOHDh3CU089ZYqnQPqoJ7V5sO/sTUddc/dJ858czERrmwbD+3lSMEsIIYRYiMkC2tdeew3//Oc/MWTIEL3asyyLDz/8EK+88gpmzJiB+Ph4fPPNNygsLBTqqmVkZGD37t34+uuvkZCQgAkTJuCTTz7B5s2bUVjY+x2eCLnR7FsiEeXrhpK6ZryzN73Ldlcr6/G29vYltw8y1/AIIYQQchOryaHNzc1FcXExkpKShOs8PDyQkJAgrMo8duwYlEqlTn23pKQkSCQSndV+N2tpaUFtba3ODyFdkTtI8faMYQCA9/Zl4HpV56t5X9p2Bs0qNSZF+eH+4aFmHCEhhBBCbmQ1AW1xcTEAwN/fX+d6f39/4bbi4mL4+fnp3O7g4AAvLy+hTWdWrFgBDw8P4SckhFaik+7dPTQEE/v7okmlxvPfn0SbWnchxd5LRfj+TD4kDIOP7hsFhmEsNFJCCCGEGBTQLlmyBAzDdPtz6dIlU421115++WXU1NQIP9euXbP0kIiVYxgG798zElIJg5/OX8d9a35Hs4qrz/f96av4yxdcceunxkdhaD/9V7cSQgghRHwG1Rh68cUXeyzNcHMpDX0FBAQAAEpKShAY2L51aElJCYYNGya0uXnrtLa2NlRWVgr374xCoYBCQbs3EcOMCvPG1icn4sG1h7H9/HVEvbYdvq6OOHu9CgAwbWAQVt093MKjJIQQQohBAa2vr2+3W8gZIyIiAgEBAdi3b58QwNbW1uL48eNCpYSxY8eiuroap06dwsiRIwEA+/fvh0aj0dnTmBCxzIgPwa/PTcZfvjiIguomFFQ3AQAW3BaLd+8eDqnEarJ2CCGEkD7LZFXg8/PzUVlZifz8fKjVamGLtaioKKFmbGxsLFasWIG7774bDMNgwYIFePPNNxEdHY2IiAi8+uqrCAoKEgoxx8XFISUlBXPnzsXnn38OlUqFefPm4aGHHtIpAk2ImCZF++PK8hm4UFiNhtY2BLg7YmSot6WHRQghhBAtkwW0S5cuxYYNG4R/Dx/OnZo9cOCAsPNGZmYmampqhDaLFi1CQ0MDnnrqKVRXV2PChAnYvXu3TqHdjRs3Yt68eZgyZYqwscLNW+ARIjZvVwUSB/j33JAQQogoUlNTcdttt6GqqgpKpbLX/eTl5SEiIgJnzpwRzgAT+2OyncKsGe0URgghRB+2uFNYT1VXli1bhuXLl5tnMEboTUA7e/ZsVFdXC/XrAUCtVqOsrAw+Pj5wcKDtya2NWDuF0f8sIYQQYkeKioqE37/77jssXboUmZmZwnU3bhXPsizUarVdB3pSqbTbhePEPtCKFkIIIcSOBAQECD8eHh5gGEb496VLl+Dm5oZdu3Zh5MiRUCgUOHz4MGbPni2sV+EtWLBASBEEAI1GgxUrViAiIgJOTk4YOnQofvjhh27H8u9//xvR0dFwdHSEv78/7rvvPuG2lpYWzJ8/H35+fnB0dMSECRNw8uTJLvtavnx5h5SBDz/8EOHh4cLtGzZswPbt24VSoqmpqcjLywPDMMJaHgA4ePAgxowZA4VCgcDAQCxZsgRtbW3C7YmJiZg/fz4WLVoELy8vBAQE2MSsdl9mv1/JCCGEELGxLKDqfPdAk5M5AyJt4rJkyRK8++67iIyMhKenfrW0V6xYgW+//Raff/45oqOjcejQITzyyCPw9fXFpEmTOrT/888/MX/+fPznP//BuHHjUFlZid9//124fdGiRdi6dSs2bNiAsLAwvPPOO0hOTkZ2dja8vLwMfk4LFy5ERkYGamtrsW7dOgCAl5cXCgsLddoVFBRg+vTpmD17Nr755htcunQJc+fOhaOjo07QumHDBrzwwgs4fvw4jh07htmzZ2P8+PGYOnWqwWMjpkcBLSGEEKIvVSOwwkJVdV4uBOQuonT1+uuvGxSYtbS04K233sLevXsxduxYAFzd+cOHD+OLL77oNKDNz8+Hi4sL7rzzTri5uSEsLExYIN7Q0IDPPvsM69evx7Rp0wAAX331Ffbs2YM1a9bgpZdeMvg5ubq6wsnJCS0tLd2mGPz73/9GSEgIPv30UzAMg9jYWBQWFmLx4sVYunQpJNpyjPHx8Vi2bBkAIDo6Gp9++in27dtHAa2VooCWEEII6WNGjRplUPvs7Gw0NjZ2COZaW1uFIPVmU6dORVhYGCIjI5GSkoKUlBTcfffdcHZ2Rk5ODlQqFcaPHy+0l8lkGDNmDDIyMgx/QgbIyMjA2LFjdRbPjR8/HvX19bh+/TpCQ0MBcAHtjQIDAzts7kSsBwW0hBBCiL5kztxMqaUeWyQuLrozvRKJBDcXPVKpVMLv9fX1AICdO3ciODhYp11XO3G6ubnh9OnTSE1NxW+//YalS5di+fLl3ebJdqenMYpNJpPp/JthGGg0GpM9HjEOBbSEEEKIvhhGtNP+1sTX1xdpaWk61509e1YI6gYOHAiFQoH8/PxO0wu64uDggKSkJCQlJWHZsmVQKpXYv38/kpOTIZfLceTIEYSFhQHggtOTJ09iwYIFXY6xuLgYLMsKs6s3LvQCALlcDrVa3e2Y4uLisHXrVp1+jhw5Ajc3N/Tr10/v50asCwW0hBBCSB83efJkrFq1Ct988w3Gjh2Lb7/9FmlpaUI6gZubGxYuXIh//vOf0Gg0mDBhAmpqanDkyBG4u7vj8ccf79Dnjh07cOXKFdx6663w9PTEL7/8Ao1Gg5iYGLi4uOCZZ57BSy+9BC8vL4SGhuKdd95BY2Mj5syZ0+kYExMTUVZWhnfeeQf33Xcfdu/ejV27dunUJw0PD8evv/6KzMxMeHt7w8PDo0M/zz77LD788EM8//zzmDdvHjIzM7Fs2TK88MILQv4ssT30P0cIIYT0ccnJyXj11VexaNEijB49GnV1dXjsscd02rzxxht49dVXsWLFCmEr+p07dyIiIqLTPpVKJX788UdMnjwZcXFx+Pzzz7Fp0yYMGjQIALBy5Urce++9ePTRRzFixAhkZ2fj119/7bLqQlxcHP79739j9erVGDp0KE6cOIGFCxfqtJk7dy5iYmIwatQo+Pr64siRIx36CQ4Oxi+//IITJ05g6NCh+Pvf/445c+bglVde6c2hI1aCdgqjncIIIYR0wRZ3CiPEloi1UxjN0BJCCCGEEJtGAS0hhBBCCLFpFNASQgghhBCbRgEtIYQQQgixaRTQEkIIIYQQm0YBLSGEENKDPlgQiBCzEGv3NdpYgRBCCOmCTCYDwzAoKyuDr6+vsLMUIcQ4LMuitbUVZWVlkEgkkMvlRvVHAS0hhBDSBalUin79+uH69evIy8uz9HAIsTvOzs4IDQ01epc2CmgJIYSQbri6uiI6OhoqlcrSQyHErkilUjg4OIhy5oMCWkIIIaQHUqkUUqnU0sMghHSBFoURQgghhBCbRgEtIYQQQgixaRTQEkIIIYQQm9Ync2j5eoK1tbUWHgkhhBBCCOkMH6fpUwe6Twa0dXV1AICQkBALj4QQQgghhHSnrq4OHh4e3bZh2D64/YlGo0FhYSHc3NzMUiS7trYWISEhuHbtGtzd3U3+eLaEjk3n6Lh0jY5N5+i4dI2OTefouHSOjkvXzH1sWJZFXV0dgoKCeqxT2ydnaCUSCfr162f2x3V3d6c/ji7QsekcHZeu0bHpHB2XrtGx6Rwdl87RcemaOY9NTzOzPFoURgghhBBCbBoFtIQQQgghxKZRQGsGCoUCy5Ytg0KhsPRQrA4dm87RcekaHZvO0XHpGh2bztFx6Rwdl65Z87Hpk4vCCCGEEEKI/aAZWkIIIYQQYtMooCWEEEIIITaNAlpCCCGEEGLTKKAlhBBCCCE2jQJaQgghhBBi0yig7aXVq1cjPDwcjo6OSEhIwIkTJ7pt//333yM2NhaOjo4YMmQIfvnlF53bWZbF0qVLERgYCCcnJyQlJSErK8uUT8EkDDkuX331FSZOnAhPT094enoiKSmpQ/vZs2eDYRidn5SUFFM/DZMw5NisX7++w/N2dHTUadMXXzOJiYkdjgvDMLjjjjuENvbwmjl06BDuuusuBAUFgWEY/PTTTz3eJzU1FSNGjIBCoUBUVBTWr1/foY2h71vWyNBj8+OPP2Lq1Knw9fWFu7s7xo4di19//VWnzfLlyzu8ZmJjY034LMRn6HFJTU3t9G+puLhYp11ffM109h7CMAwGDRoktLGH18yKFSswevRouLm5wc/PDzNnzkRmZmaP97PWeIYC2l747rvv8MILL2DZsmU4ffo0hg4diuTkZJSWlnba/ujRo3j44YcxZ84cnDlzBjNnzsTMmTORlpYmtHnnnXfw8ccf4/PPP8fx48fh4uKC5ORkNDc3m+tpGc3Q45KamoqHH34YBw4cwLFjxxASEoLbb78dBQUFOu1SUlJQVFQk/GzatMkcT0dUhh4bgNta8MbnffXqVZ3b++Jr5scff9Q5JmlpaZBKpbj//vt12tn6a6ahoQFDhw7F6tWr9Wqfm5uLO+64A7fddhvOnj2LBQsW4Mknn9QJ3HrzGrRGhh6bQ4cOYerUqfjll19w6tQp3Hbbbbjrrrtw5swZnXaDBg3Sec0cPnzYFMM3GUOPCy8zM1Pnefv5+Qm39dXXzEcffaRzTK5duwYvL68O7zO2/po5ePAgnnvuOfzxxx/Ys2cPVCoVbr/9djQ0NHR5H6uOZ1hisDFjxrDPPfec8G+1Ws0GBQWxK1as6LT9Aw88wN5xxx061yUkJLBPP/00y7Isq9Fo2ICAAHbVqlXC7dXV1axCoWA3bdpkgmdgGoYel5u1tbWxbm5u7IYNG4TrHn/8cXbGjBliD9XsDD0269atYz08PLrsj14znA8++IB1c3Nj6+vrhevs5TXDA8Bu27at2zaLFi1iBw0apHPdgw8+yCYnJwv/NvZYWyN9jk1nBg4cyL722mvCv5ctW8YOHTpUvIFZmD7H5cCBAywAtqqqqss29JrhbNu2jWUYhs3LyxOus7fXDMuybGlpKQuAPXjwYJdtrDmeoRlaA7W2tuLUqVNISkoSrpNIJEhKSsKxY8c6vc+xY8d02gNAcnKy0D43NxfFxcU6bTw8PJCQkNBln9amN8flZo2NjVCpVPDy8tK5PjU1FX5+foiJicEzzzyDiooKUcduar09NvX19QgLC0NISAhmzJiBixcvCrfRa4azZs0aPPTQQ3BxcdG53tZfM4bq6T1GjGNtLzQaDerq6jq8z2RlZSEoKAiRkZGYNWsW8vPzLTRC8xo2bBgCAwMxdepUHDlyRLieXjPt1qxZg6SkJISFhelcb2+vmZqaGgDo8LdxI2uOZyigNVB5eTnUajX8/f11rvf39++Qe8QrLi7utj1/aUif1qY3x+VmixcvRlBQkM4fQkpKCr755hvs27cPb7/9Ng4ePIhp06ZBrVaLOn5T6s2xiYmJwdq1a7F9+3Z8++230Gg0GDduHK5fvw6AXjMAcOLECaSlpeHJJ5/Uud4eXjOG6uo9pra2Fk1NTaL8fdqLd999F/X19XjggQeE6xISErB+/Xrs3r0bn332GXJzczFx4kTU1dVZcKSmFRgYiM8//xxbt27F1q1bERISgsTERJw+fRqAOO/p9qCwsBC7du3q8D5jb68ZjUaDBQsWYPz48Rg8eHCX7aw5nnEwae+E6GnlypXYvHkzUlNTdRY/PfTQQ8LvQ4YMQXx8PPr374/U1FRMmTLFEkM1i7Fjx2Ls2LHCv8eNG4e4uDh88cUXeOONNyw4MuuxZs0aDBkyBGPGjNG5vq++ZkjP/vvf/+K1117D9u3bdXJFp02bJvweHx+PhIQEhIWFYcuWLZgzZ44lhmpyMTExiImJEf49btw45OTk4IMPPsB//vMfC47MumzYsAFKpRIzZ87Uud7eXjPPPfcc0tLSbC4P+EY0Q2sgHx8fSKVSlJSU6FxfUlKCgICATu8TEBDQbXv+0pA+rU1vjgvv3XffxcqVK/Hbb78hPj6+27aRkZHw8fFBdna20WM2F2OODU8mk2H48OHC8+7rr5mGhgZs3rxZrw8OW3zNGKqr9xh3d3c4OTmJ8hq0dZs3b8aTTz6JLVu2dDhlejOlUokBAwbY9WumM2PGjBGeM71muNX6a9euxaOPPgq5XN5tW1t+zcybNw87duzAgQMH0K9fv27bWnM8QwGtgeRyOUaOHIl9+/YJ12k0Guzbt09nRu1GY8eO1WkPAHv27BHaR0REICAgQKdNbW0tjh8/3mWf1qY3xwXgVkO+8cYb2L17N0aNGtXj41y/fh0VFRUIDAwUZdzm0NtjcyO1Wo0LFy4Iz7svv2YArmxMS0sLHnnkkR4fxxZfM4bq6T1GjNegLdu0aRP+9re/YdOmTTol3rpSX1+PnJwcu37NdObs2bPCc+7rrxmAqwKQnZ2t1xdnW3zNsCyLefPmYdu2bdi/fz8iIiJ6vI9VxzMmXXJmpzZv3swqFAp2/fr1bHp6OvvUU0+xSqWSLS4uZlmWZR999FF2yZIlQvsjR46wDg4O7LvvvstmZGSwy5YtY2UyGXvhwgWhzcqVK1mlUslu376dPX/+PDtjxgw2IiKCbWpqMvvz6y1Dj8vKlStZuVzO/vDDD2xRUZHwU1dXx7Isy9bV1bELFy5kjx07xubm5rJ79+5lR4wYwUZHR7PNzc0WeY69Zeixee2119hff/2VzcnJYU+dOsU+9NBDrKOjI3vx4kWhTV98zfAmTJjAPvjggx2ut5fXTF1dHXvmzBn2zJkzLAD2/fffZ8+cOcNevXqVZVmWXbJkCfvoo48K7a9cucI6OzuzL730EpuRkcGuXr2alUql7O7du4U2PR1rW2Hosdm4cSPr4ODArl69Wud9prq6Wmjz4osvsqmpqWxubi575MgRNikpifXx8WFLS0vN/vx6y9Dj8sEHH7A//fQTm5WVxV64cIH9xz/+wUokEnbv3r1Cm776muE98sgjbEJCQqd92sNr5plnnmE9PDzY1NRUnb+NxsZGoY0txTMU0PbSJ598woaGhrJyuZwdM2YM+8cffwi3TZo0iX388cd12m/ZsoUdMGAAK5fL2UGDBrE7d+7UuV2j0bCvvvoq6+/vzyoUCnbKlClsZmamOZ6KqAw5LmFhYSyADj/Lli1jWZZlGxsb2dtvv5319fVlZTIZGxYWxs6dO9fm3kx5hhybBQsWCG39/f3Z6dOns6dPn9bpry++ZliWZS9dusQCYH/77bcOfdnLa4YvqXTzD38sHn/8cXbSpEkd7jNs2DBWLpezkZGR7Lp16zr0292xthWGHptJkyZ1255luRJngYGBrFwuZ4ODg9kHH3yQzc7ONu8TM5Khx+Xtt99m+/fvzzo6OrJeXl5sYmIiu3///g799sXXDMtypaacnJzYL7/8stM+7eE109kxAaDz3mFL8QyjfVKEEEIIIYTYJMqhJYQQQgghNo0CWkIIIYQQYtMooCWEEEIIITaNAlpCCCGEEGLTKKAlhBBCCCE2jQJaQgghhBBi0yigJYQQQgghNo0CWkIIIYQQYtMooCWEEEIIITaNAlpCCCGEEGLTKKAlhBBCCCE27f8DGkIXBc+gvQwAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -278,7 +70292,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABdEAAAHqCAYAAADrpwd3AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnXmcU9XB/p8kM0lmH5ZhZlgHUDZlUVTEatVXKlir0r7ur6VSlxal1fKrC9WKS5Vqq7WLlUpFtNpibdX6VosKr7giyKaioLKvMzDD7EtmJrm/Pw4nN5nJnrsmz/fzySeZzM29Jzk59+Q+5znPcSiKooAQQgghhBBCCCGEEEIIIb1wml0AQgghhBBCCCGEEEIIIcSqUEQnhBBCCCGEEEIIIYQQQqJAEZ0QQgghhBBCCCGEEEIIiQJFdEIIIYQQQgghhBBCCCEkChTRCSGEEEIIIYQQQgghhJAoUEQnhBBCCCGEEEIIIYQQQqJAEZ0QQgghhBBCCCGEEEIIiQJFdEIIIYQQQgghhBBCCCEkChTRCSGEEEIIIYQQQgghhJAoUEQnhBBCCCGEEEIIIYQQQqJAEZ0QQgghhBBCCCGEEEIIiQJFdEJITJYuXQqHw4Fdu3bZYr968NBDD2HMmDEIBAJJv3bRokUYOnQofD6fDiUjhBBCBOyv45Nqf86+nBBide6++244HA7U1tYadkw79Q/y8wklWvk/+ugjnHbaaSgoKIDD4cCmTZsiPpftVFVV4e67705rH9nYL+ulLWhRHyQ+FNEJSRCHw5HQbdWqVWYX1VJ88MEHuPvuu9HQ0GB2UVKiqakJDz74IG677TY4ncmfMq+++mp0dnbiT3/6kw6lI4QQEoq8IJY3r9eLUaNGYe7cuaipqem13bp168L+9nq92L9/f6/9nnXWWTj++OMjHiuZ11gZu/fX8UinP2dfTggxmz/+8Y9wOByYMmWK2UXRBKv2OV1dXbjkkktw5MgR/OY3v8Ff/vIXDBo0qNdzw4YNM7uoticb+2VqC/aHIjohCfKXv/wl7PaNb3wj4vNjx441uaTW4oMPPsA999zT6wfSd7/7XbS3t1v+B8iSJUvQ3d2NK664IqXXe71efO9738MjjzwCRVE0Lh0hhJBI3HvvvfjLX/6CP/zhDzjttNPw+OOPY+rUqWhra4v5Op/Ph1/+8pdJHSuV11gRu/fX8UinP2dfTggxm+eeew5VVVVYu3Yttm3bZnZx0iZan2Mkkfq37du3Y/fu3fjpT3+K66+/HldddRXq6up6PdenTx/Typ0pZGO/TG3B/lBEJyRBrrrqqrDbqFGjIj5fXl4e8fWtra1GFtfyuFwueL3eXtPqrMZTTz2FCy+8EF6vN+V9XHrppdi9ezfeeustDUtGCCEkGueddx6uuuoqXHvttVi6dCluvvlm7Ny5E//6179ivm7SpElYvHgxDhw4kPCxUnmNnbBLfx2PdPtz9uWEELPYuXMnPvjgAzzyyCMoKyvDc889Z3aRMoJI/duhQ4cAAKWlpTGf04Js1weysV+mtmB/KKITogMyc+3zzz/HlVdeiT59+uD000/H1Vdfjaqqqqjb92T//v34/ve/j/Lycng8Hhx33HFYsmRJQmVobm7GzTffjKqqKng8HgwYMADf+MY3sGHDhuA2GzduxHnnnYfi4mIUFhbinHPOwYcffhh334m+j7vvvhu33HILAGD48OHB6fW7du2KmkGXSJnkcbZt24arr74apaWlKCkpwezZs+O6DCVlZWWYO3dur+dPOukknH/++QDED9ZPPvkE06ZN67Xd/v374fV68f3vfz/s+RUrViA3Nxc/+clPgs9NnjwZffv2jSveEEII0Yf/+q//AiDO67H42c9+Br/fn5SzPJXXhML+OjaJ9NfxiNafsy8nhNiB5557Dn369MH555+Piy++OKaIXltbi0svvRTFxcXo168fbrrpJnR0dAT/n0ifA+jb78TqcyTpXAe/9957OPnkk+H1ejFy5Mio0Rc9+7err74aZ555JgDgkksugcPhQFVVVa/nzjrrrKTLGU0fSGUfifSp+/fvxzXXXIOBAwfC4/Fg+PDhmDNnDjo7OzX5jKORaJ9tt35Zz98igHXfN+lNjtkFICSTueSSS3DsscfigQcegKIoWLt2bcKvrampwamnngqHw4G5c+eirKwM//nPf3DNNdegqakJN998c8zX//CHP8Q//vEPzJ07F+PGjUNdXR3ee+89bNmyBSeeeCI+++wznHHGGSguLsatt96K3Nxc/OlPf8JZZ52Ft99+W5O8ve985zv48ssv8be//Q2/+c1v0L9/fwCiE4pEsmW69NJLMXz4cCxcuBAbNmzAn//8ZwwYMAAPPvhgzHIdOHAAtbW1mDhxYtjzfr8fn332WTCq54MPPgAAnHjiib32MWjQIFx77bV44oknsGDBAgwbNgxbt27FJZdcgvPOOw8PP/xw2PYnnngi3n///ZjlIoQQog/bt28HAPTr1y/mdsOHD8esWbOwePFi3H777Rg4cGDcfafymlDYX0cn0f46HtH6c/blhBA78Nxzz+E73/kO3G43rrjiCjz++OP46KOPcPLJJ/fa9tJLL0VVVRUWLlyIDz/8EL/73e9QX1+PZ555BkD8PgdI/hyfLPH6nHSugz/99FOce+65KCsrw913343u7m4sWLAg6mzxUH7wgx9g0KBBeOCBB/DjH/8YJ598Mnbu3ImOjo6w5+S+UilnT30glX3E61MPHDiAU045BQ0NDbj++usxZswY7N+/H//4xz/Q1tYGt9udttYQiWT6bDv1y3r/FgGs+b5JFBRCSErceOONSrQmtGDBAgWAcsUVV4Q9/73vfU8ZNmxY1O1Dueaaa5TKykqltrY27PnLL79cKSkpUdra2mKWr6SkRLnxxhuj/n/mzJmK2+1Wtm/fHnzuwIEDSlFRkfL1r389+NxTTz2lAFB27tyZ0vv41a9+1ev10fabaJnkcb7//e+H7fPb3/620q9fv6jvWfKf//xHAaCsWbMm7PnNmzcrAJTnnntOURRFufPOOxUASnNzc8T97Nu3T/F4PMqcOXOU2tpaZeTIkcqkSZOUlpaWXttef/31Sl5eXtyyEUIISR3Zt6xYsUI5fPiwsnfvXmXZsmVKv379lLy8PGXfvn1h23300Ue9/t6+fbuSk5Oj/PjHPw7u98wzz1SOO+64iMdK5jWRYH8dnUT763jE6s/ZlxNCrMy6desUAMqbb76pKIqiBAIBZfDgwcpNN90Utp0831544YVhz99www0KAOXjjz9WFCV+n6MoxvQ70focRUnvOnjmzJmK1+tVdu/eHXzu888/V1wuV69+L1L533rrLQWA8sILL8R8LtlyRtMHUtlHvD511qxZitPpDP7GCSUQCCR93GgMGzZMWbBgQfDvZPpsO/XLRvwWUZT033fP+iD6wDgXQnTkhz/8YUqvUxQF//znP3HBBRdAURTU1tYGb9OnT0djY2OvKXc9KS0txZo1ayJmtPr9frzxxhuYOXMmRowYEXy+srISV155Jd577z00NTWlVPZUSaVMPT/fM844A3V1dXHL/sknn8DpdOL4448Pe/7jjz8GAIwfPx4AUFdXh5ycHBQWFkbcz6BBg3DddddhyZIlOP/889He3o5///vfKCgo6LVtnz590N7envD0dUIIIakzbdo0lJWVYciQIbj88stRWFiIl156CYMGDYr72hEjRuC73/0unnjiCRw8eDCh46XyGgn76+gk2l/HI1Z/zr6cEGJlnnvuOZSXl+Pss88GADgcDlx22WVYtmwZ/H5/r+1vvPHGsL9/9KMfAQBee+01ALH7HMD8fied62C/34/XX38dM2fOxNChQ4PPjx07FtOnT7dEOUP7Qy32AYT3qYFAAC+//DIuuOACnHTSSb1e63A4NNEaIpFMn21UvxwIBNDR0ZHQTYmyUKcRv0W0ft9EPyiiE6Ijw4cPT+l1hw8fRkNDA5544gmUlZWF3WbPng1AXeAkGg899BA2b96MIUOG4JRTTsHdd9+NHTt2BPff1taG0aNH93rd2LFjEQgEsHfv3pTKniqplCn0xxGA4Crp9fX1MY/18ccf45hjjkF+fn7Y85s2bUJubi7GjBmTcLl/+tOfwufz4ZNPPsErr7wSVaCRnbLdF2YjhBA78Nhjj+HNN9/EW2+9hc8//xw7duxI6gL6zjvvRHd3d1I556m8BmB/HYtk+ut3330XJ5xwAgoKCnDhhReivb094ffEvpwQYkX8fj+WLVuGs88+Gzt37sS2bduwbds2TJkyBTU1NVi5cmWv1xx77LFhf48cORJOpzOY+x2rzwHM73fSuQ4+fPgw2tvbe30GACK+HzPKGaoPpLqPWH3q4cOH0dTU1Evw1aLs8bDiNfY777yDvLy8hG5ffPGFJu+Lv0cyG2aiE6IjeXl5YX9HO8n1dBEEAgEAwFVXXYXvfe97EV8zYcKEmMe+9NJLccYZZ+Cll17CG2+8gV/96ld48MEH8eKLL+KEE05I9C1EJNH3oTculyvi89FGkSWffvppr0wzAPjoo48wevRo5ObmAhDZud3d3WhubkZRUVHEfd1///0AgO7ubvTt2zfqMevr65Gfn9/rO0EIIUR7TjnllIgOrEQZMWIErrrqKjzxxBO4/fbbdXsNwP46Fon211999RWuuOIK/O1vf8OkSZNw9tln4y9/+Quuv/56APH7c/blhBAr8n//9384ePAgli1bhmXLlvX6/3PPPYdzzz035j569gOx+pzzzjsv7TKn2+9ocR1sBKmWM7T/SHUfqfap6R43Hon22YBx/fKYMWPw1FNPJVT+ysrKiM8n875i/R6htpAZUEQnxED69OmDhoaGXs/v3r077O+ysjIUFRXB7/dHXL05USorK3HDDTfghhtuwKFDh3DiiSfi/vvvx9tvv438/PyIo61bt26F0+nEkCFD0n4fQOKjo2VlZWmVKVECgQC++OILfPvb3w57/tChQ3jvvfdw6aWXBp+To8o7d+6M+EPiV7/6Ff785z/jD3/4A2655Rbcf//9+POf/xzxuDt37sTYsWPTLj8hhBBjuPPOO/Hss8/GXfwy3dcA7K8jkUx/PW/ePNx2220444wzAAAzZ87EunXrgiJ6rP6cfTkhxKo899xzGDBgAB577LFe/3vxxRfx0ksvYdGiRWFC2ldffRXmdt62bRsCgQCqqqqCz0Xrc84777y0z/GJ9jvR+px0roPLysqQl5eHr776qtf/ormMU0WL63Wtrvl77rO4uBibN2829LjJ9NmAcf1yRUUFrr766hTekSDZ9xXr9wi1hcyAcS6EGMjIkSPR2NiITz75JPjcwYMH8dJLL4Vt53K58N///d/45z//GbEDPHz4cMzj+P1+NDY2hj03YMAADBw4ED6fDy6XC+eeey7+9a9/Baf2AWKF8b/+9a84/fTTUVxcnPb7ABDM8Ir0YyqUdMuUKH6/H11dXWH5Yd3d3fjBD36A7u7usEyzqVOnAgDWrVvXaz8vv/wybr/9dtx333248cYbcf311+OZZ57Bzp07Ix53w4YNOO2009IuPyGEEGMYOXIkrrrqKvzpT39CdXW1Lq9hfx2dRPvrI0eOYMWKFbjqqquC2wUCAXi93uDf0fpz9uWEEKvS3t6OF198Ed/61rdw8cUX97rNnTsXzc3NeOWVV8Je11Nw//3vfw8AOO+88+L2OUD65/hE+51ofU4618EulwvTp0/Hyy+/jD179gSf37JlC15//fWor0uFdK/XtdpHT5xOJ2bOnIn//d//jXgNqyiKLsdN5hobsE+/nMz7ivd7hNpCZkAnOiEGcvnll+O2227Dt7/9bfz4xz9GW1sbHn/8cYwaNarX4h2//OUv8dZbb2HKlCm47rrrMG7cOBw5cgQbNmzAihUrcOTIkajHaW5uxuDBg3HxxRdj4sSJKCwsxIoVK/DRRx/h4YcfBgD84he/wJtvvonTTz8dN9xwA3JycvCnP/0JPp8PDz30kGbvY/LkyQCAO+64A5dffjlyc3NxwQUXRNxvOmVKlNzcXEyYMAGPP/54MP/shRdeCDo4QjvCESNG4Pjjj8eKFSvw/e9/P/j8+vXr8T//8z/4n//5H9xxxx0AgFtvvRWLFi2KOGK8fv16HDlyBBdddJEm74EQQogx3HHHHfjLX/6CL774Ascdd5zmr2F/HZ1E++uVK1eiq6srzHnZ3t4enBINRO7P2ZcTQqzMK6+8gubmZlx44YUR/3/qqaeirKwMzz33HC677LLg8zt37sSFF16IGTNmYPXq1Xj22Wdx5ZVXYuLEiWhoaIjb5wDG9DvR+pyCgoK0roPvueceLF++HGeccQZuuOEGdHd34/e//z2OO+64MGFfC9Ipp5b76MkDDzyAN954A2eeeSauv/56jB07FgcPHsQLL7yA9957D6WlpZofN5lrbMA+/XIy7yve7xFqCxmCQghJiRtvvFGJ1oQWLFigAFAOHz7c639vvPGGcvzxxytut1sZPXq08uyzzwa370lNTY1y4403KkOGDFFyc3OViooK5ZxzzlGeeOKJmGXz+XzKLbfcokycOFEpKipSCgoKlIkTJyp//OMfw7bbsGGDMn36dKWwsFDJz89Xzj77bOWDDz4I2+app55SACg7d+5M+X3cd999yqBBgxSn0xncV7T9JlKmaJ9vtH32ZMOGDcrkyZMVr9erHHfcccoTTzyhPPnkkwoAZffu3WHbPvLII0phYaHS1tamKIqi7N27V6msrFS+9rWvKR0dHWHbzpkzR8nNzVV27NgR9vxtt92mDB06VAkEAjHLRQghJD1kP/DRRx8ltV2s133ve99TACjHHXdcwseK9pqesL8O32dPEumvf/WrXylXXXVV2OuGDRumfPLJJ2HPhfbn7MsJIVbnggsuULxer9La2hp1m6uvvlrJzc1Vamtrg+fbzz//XLn44ouVoqIipU+fPsrcuXOV9vZ2RVES73MUxZh+J1KfI0n1OlhRFOXtt99WJk+erLjdbmXEiBHKokWLIh4/UvnfeustBYDywgsvxHwu2XLG0gfS3Uek97F7925l1qxZSllZmeLxeJQRI0YoN954o+Lz+ZI+bjSGDRumLFiwIPh3MtfYimKffjnR95XI7xE9tYWe9UH0waEoCa4+QAghWUhjYyNGjBiBhx56CNdcc03Sr/f5fKiqqsLtt9+Om266SYcSEkIIIdnLL37xC+zduxd/+tOfAAgn2P/7f/8PmzZtCtsunf6cfTkhhBASTlVVFa6++mrcfffdKb0+0/rlRH6P6KktpFsfJDGYiU4IITEoKSnBrbfeil/96lfBlcyT4amnnkJubi5++MMf6lA6QgghJLuZPHkyVq5cibq6OmzZsgU33nhjxEX40unP2ZcTQggh2pJp/XIiv0eoLdgfOtEJIYQQQgghtiQQCOD73/8+XnjhBQwaNAgPPfQQZs6caXaxCCGEkIyGzudwzP49wvowBi4sSgghhBBCCLElTqcTS5cuxdKlS80uCiGEEEKyFP4eyQ7oRCeEEEIIIYQQQgghhBBCosBMdEIIIYQQQgghhBBCCCEkChTRCSGEEEIIIYQQQgghhJAoZGUmeiAQwIEDB1BUVASHw2F2cQghhJCIKIqC5uZmDBw4EE5ndo97s+8mhBBiB9h3q7DvJoQQYgcS7buzUkQ/cOAAhgwZYnYxCCGEkITYu3cvBg8ebHYxTIV9NyGEEDvBvpt9NyGEEHsRr+/OShG9qKgIgPhwiouLTS4NIYQQEpmmpiYMGTIk2G9lM+y7CSGE2AH23SrsuwkhhNiBRPvurBTR5VSy4uJiduaEEEIsD6dAs+8mhBBiL9h3s+8mhBBiL+L13dkd0kYIIYSQMN555x1ccMEFGDhwIBwOB15++eW4r1m1ahVOPPFEeDweHHPMMVi6dGmvbR577DFUVVXB6/ViypQpWLt2rfaFJ4QQQkhE9OrfCSGEkGyBIjohhBBCgrS2tmLixIl47LHHEtp+586dOP/883H22Wdj06ZNuPnmm3Httdfi9ddfD27z/PPPY968eViwYAE2bNiAiRMnYvr06Th06JBeb4MQQgghIejRvxNCCCHZhENRFMXsQhhNU1MTSkpK0NjYyGllhBBCLIvZ/ZXD4cBLL72EmTNnRt3mtttuw6uvvorNmzcHn7v88svR0NCA5cuXAwCmTJmCk08+GX/4wx8AAIFAAEOGDMGPfvQj3H777QmVxezPghBCCEkEO/RXWvXv8bDDZ0EIIYQk2l9lZSY6IYQQ/fD7/ejq6jK7GLYgNzcXLpfL7GKkxerVqzFt2rSw56ZPn46bb74ZANDZ2Yn169dj/vz5wf87nU5MmzYNq1evjrpfn88Hn88X/LupqUnbghNCSBbCPjp9MqHvToR4/TshhFgN9nEkGlr13RTRCSGEaIKiKKiurkZDQ4PZRbEVpaWlqKiosO0CZNXV1SgvLw97rry8HE1NTWhvb0d9fT38fn/EbbZu3Rp1vwsXLsQ999yjS5kJISTbYB+tLXbvuxMhXv+el5fX6zUcACeEmAH7OJIIWvTdFNEJIYRogvzhMmDAAOTn52f0haUWKIqCtra2YC54ZWWlySWyFvPnz8e8efOCfzc1NWHIkCEmlogQQuwL+2htYN8dGw6AE0LMgH0ciYWWfTdFdEIIIWnj9/uDP1z69etndnFsg3RxHTp0CAMGDLDl9PCKigrU1NSEPVdTU4Pi4mLk5eXB5XLB5XJF3KaioiLqfj0eDzwejy5lJoSQbIJ9tLZkQt+dCPH690hwAJwQYjTs40giaNV3O7UsFCGEkOxEZs/l5+ebXBL7IT8zu+b3TZ06FStXrgx77s0338TUqVMBAG63G5MnTw7bJhAIYOXKlcFtCCGE6Af7aO2xe9+dCPH690h4PB4UFxeH3QghRE/Yx5FE0aLvpohOCCFEMzh1Lnms9pm1tLRg06ZN2LRpEwBg586d2LRpE/bs2QNAuMxmzZoV3P6HP/whduzYgVtvvRVbt27FH//4R/z973/HT37yk+A28+bNw+LFi/H0009jy5YtmDNnDlpbWzF79mxD3xshhGQzVutv7IwdP0s9+ndCCLEKdjwvE2PR4jvCOBdCCCGEBFm3bh3OPvvs4N9yWvb3vvc9LF26FAcPHgxecAPA8OHD8eqrr+InP/kJfvvb32Lw4MH485//jOnTpwe3ueyyy3D48GHcddddqK6uxqRJk7B8+fJeC5YRQgghRB/06N8JIYSQbIIiOiGEEEKCnHXWWVAUJer/ly5dGvE1GzdujLnfuXPnYu7cuekWjxBCCCEpoFf/TgghhGQLjHMhhBBCCCGEEJKR1NXVYcCAAdi1a1dC219++eV4+OGH9S0UIYQQQmwHRXRCCCEkAZK9CAd4IU4IIYSYzf3334+LLroIVVVVCW1/55134v7770djY6O+BSOEEJL1rFq1KuH+KZRMujbV6r2k+lkmA0V0QgghJAGSvQgHeCFOCCGEmElbWxuefPJJXHPNNQm/5vjjj8fIkSPx7LPP6lgyQgghJHUy6drUTu+FIjohhBASh1QuwgFeiBNCCCHpsnz5chQUFCAQCASf27x5MxwOB2pra2O+9rXXXoPH48Gpp54afO5vf/sb8vLycPDgweBzs2fPxoQJE4IX4xdccAGWLVum8TshhBBC0ieTrk3t9l4oopOU+eor4OGHgU8/Nbsk2UtTE/D73wOvvmp2SbKXQAD461+BP/8Z6Ow0uzTWQlGA1lZjbjU1wI4dQEOD+DvGulkRiXeBHukiHOCFOMkcfN0+PLH+CTyx/gn4un1mF4cQ0/ik5hMsfHch1h9Yb3ZRyFE2btyI448/Hk6neum6adMmDBw4EP3794/52nfffReTJ08Oe+7yyy/HqFGj8MADDwAAFixYgBUrVuA///kPSkpKAACnnHIK1q5dC5+P50NCSAp0dgJPPw28+67ZJclOjLwQ7XlL9kK0B4kMHKc6QAzoc20as8x79gDV1UB3d8TX2u06O8fQo5GM4cAB4NRTgSNHgPx8YP16YMwYs0uVXSgK8K1vqf3y0qXA975napGyknvvBe65Rzx+5x3gmWfMLY+VaGsDCgvNOXZLC1BQkPj28S7QI12EA+JC/Je//CUeeOAB/P73vw9eiH/44YdhF+L3338/fD4fPB5P2u+NED34nxf/B//c8k8AwH+2/QcvXfaSySUixHg+rfkUU/48BR3dHViwagHe+t5b+NrQr5ldLF1QFAVtXW2GHzc/Nx8OhyOp12zatAkTJ04Me+7jjz8OPtfZ2Ykbb7wR77//PkpKSvDqq6+ib9++AIDdu3dj4MCBYa91OBy4//77cfHFF6OiogK///3v8e6772LQoEHBbQYOHIjOzk5UV1dj2LBhqbxVQkg2c/PNwOOPi8crVgDnnGNqcbIOO12I9iCRgeNoA8TxrksBfa5NY5a5tlY4DxsagNGjgR6/Aex2nU0RnaTEI48IAR0Q56df/AKw0IyQrOD118MHtu+4A/if/wFy2KoNo74eePBB9e+//AW47TbguOPMKxNJjXgX6JEuwgFeiJPMYPXe1UEBHQBe3voy3t71Ns6sOtPEUhFiPD/7v5+ho7sDANAV6MJPXv8J1l631uRS6UNbVxsKFxovMLTMb0GBOzlxYePGjfjxj38c9tymTZtw0kknAQDuuecenH766Vi8eDHuvPNOLFu2DDfccAMAoL29HV6vt9c+v/Wtb2HcuHG499578cYbb+C4Hj/e8vLyAIhp5oQQkhQNDcCSJerfDz9MEZ0kTLzrUiD1AWJAn2vTqGUePVoI6AA66+tx4+zZeH/t2rABb7tdZzPOhSSNogB//7t4PH++uP/Xv4CODvPKlI288IK4v/ZaoF8/YP9+4P33zS1TtvHPf4rv/fjxwEUXief++ldzy2Ql8vPFQLyet7o6MQPgnXeArVvF/fr14tjJsHHjRkyYMCHsudAfA9EuwoHwC/GXXnqJF+LEdvx2zW8BALMnzcYNJwnh6cH3H4z1EkIyjm1HtuHfX/4bTocT781+D26XGx8d+Ahr9q0xu2hZTWtrK7Zv3x52cR4IBLBx40ZMnDgRjY2NeOedd/C9o9Mxhw8fjh07dgS37d+/P+rr63vtd/ny5di6dSv8fj/Ky8t7/f/IUbdQWVmZ1m+JEJLpvP8+EBoFtWKFuHAhxmHEhWi0W7IXoj2Id10KJDZAHOm6FIh9bXr77bfD4XDEvG3dujXxMo8cGfz7nsWLcfqECfj8889xzjnnBGNY7HadTc8qSZpPPwX27gXy8oA77xTu2337gDffBC64wOzSZQd+P/DKK+LxFVeIPvovfwH+93+BM2kcNIwVK8T9d74DjBghBpP+8x/g/vvNLZdVcDjSmsmWEG1t4lxUWAgMHw40N4vB7q4uwO1ObB+xLtDlAifRLsIBXogTe+Pr9uHfX/4bAHDDyTeg2FOMP677I97c8Sbq2+vRJ6+PySUkxBj+tfVfAICzq87G14Z+DZcffzme+fgZPPvJs5gyeIrJpdOe/Nx8tMw3XtTJz01OXNi5cycCgQDGhORGvv7666irq8PEiROxYsUKbNu2DZMmTQIA1NTU4Ec/+lFw2xNOOKHXomMbNmzApZdeiieffBJLly7Fz3/+c7wg3SlH2bx5MwYPHhw3c50QQnqx9ugMplmzhMNn1y5g9WrgG98wtVhZhREXojqQyHUpkPoAMRD72vT//b//h6uvvjpmGUeMGJF4mc8+GwDQWFiIdzZuxP233gpADHhv2bIl5ntJ5P2YcZ1NJzpJmtdfF/f/9V9ikO3CC8XfUlAk+vPJJ0BtLVBcDHz96+rgxWuvmVuubCIQAFauFI/POQeYPl083rhRrJtBjKGpSdwXFwvRXA78J2P2iHeBDoiL8M8//7zXa0MvxM855xz8/Oc/77UNL8SJlXl/7/to7WpFeUE5Tqw8EaP6jcJxZcehO9CNV7/iqtUke3jlS+FOuGi0mFr232P/GwDw6levQklzkTAr4nA4UOAuMPyWbB56v3794HA48NFHHwEAPvzwQ8ydOxderxejRo3Cxx9/jIceegibNm3Cpk2bMGnSpLAL+enTp+Ozzz4LXqDv2rUL559/Pn72s5/hiiuuwL333ot//vOf2LBhQ9hx3333XZx77rlpfsqEkKxEiuhTpgCnnCIe9zjHEBKJRK5LgcjXpolclwKxr03LysowZsyYmDd3D6dazDIfeyzgcmHFxo3YtncvJl10ESZNmoQ777wzuHaJ3a6zKaKTpDn6GxZf/7q4/9rR9ZbWcLarYcg6OPlkkYEu3edbtgAhCy8THdm6VQxk5OeL30fl5YCcwbR6tbllyyakWF5UJO7l+jHJiOjxLtCB3hfhAC/ESWbwxvY3AADTj5kOp0P8LJQi4uvbXzetXIQYia/bhw/3fQgAOO/Y8wAA5ww/B26XGzsbdmJrbe+py8QYKisrcd999+Gqq67CsGHDsGjRIlxyySU4/vjj4XK50NDQEFxMrKmpCRs3bsTZR51vADB+/HiceOKJ+Pvf/44jR45gxowZuOiii3D77bcDAKZMmYLzzjsPP/vZz4Kv6ejowMsvv4zrrrvO2DdLCMkMQi/WTzxRPN640bzyENuQyHUpkPoAMaD9tWnMMg8dCuTn4+PPP8dDN92ETc89h03vvx824G2362yK6CRp1q8X97I/mHJ0huvGjeHRX0Q/QvtlABgwABg6VDyW9UP0ZdMmcT9pkhobcnR9K9aBQXR1iRugOtDlrL3W1sT3E+8CHQi/CAfAC3GSMazZL0bAzxymZoGdVXUWAODd3e9GegkhGcfG6o3o9HeiLL8MI/uI/M4CdwFOH3o6AOCd3e+YWbys54477sCRI0ewe/duLF26FL/85S+DF+vHHHMM1h51fd5777348Y9/jPweebR33XUXfvvb36K0tBRbt27FokWLwv7/6quvYvny5cG/n3rqKZxyyik49dRTdX5nhJCMo75eLNoEAMcdB5xwgnhMJzpJgESuS4HUBogBfa5No5Z51ChR5rw8NDQ2wnPU9dZUUxM24G2362xmopOkqK8H5Fo9UkQfMQLo31+4cj/+WJ2xRPSjp4gOiM99zx5g3ToRtUP05ZNPxH3o+hmTJ4uF2CmiG4NcP8TrBeRvCulEb2sTkTvOBIeK77jjDtxxxx0xt7nrrrtwyy234LrrrkPfvn0jLqry6qvh8Re8ECdWJqAEsP6AOGGdPFDtUKYOmQqXw4Xdjbuxp3EPhpYMNauIhBiCdKGfOvjUsLiRrw35Gv5v5//hg30f4Acn/cCs4pEYXHnllZgxYwaOPfZYzJgxA7fddluvbc4//3x89dVX2L9/P4YMGRJ3n7m5ufj973+vR3EJIZmOFEvKy4XLR4roX30lLlDSXHSSZD6JXJcC4demiVyXAvpdm0Ys8zXXiJiEvDwx4L15My49/XTc++CDvQa87XSdTRGdJMXHH4v7qirgaIQRHA4hqL/xhlh0lCK6vvj9IrYFUPtkQLig//EPDnIbhWwLIdFkmDxZ3K9bByiKaBtEP6SIfnRRbgBiVoDTKQR0ny/8f+mS7EU4wAtxYm2+qvsKzZ3NyMvJw9iyscHnC92FOKHyBKw7sA4f7P2AIjrJeNbuF07mKYPCFxA9bchpAIAP9n5geJlIYvTv3x/r1q2Lu93NN9+c8D6vvfbaNEpECMlqdu4U93LxxbIyoE8f1Y14/PHmlY1kFJa/Nu3sFPdutxjwPuccHLt8OWacdVavAW/Lv5cQKKKTpPjiC3F/3HHhz48bJ0T0COsBEI3ZtUucj7xeYNgw9XlZJ1JgJ/oSyYk+frwQzmtrgcOHRcwO0Y+ODnEfauhwOIRw3toKtLdrK6IDyV2EA7wQJ9Zmw0Ex6jqpYhJynOE/CSdXTsa6A+vwcfXHuPz4y80oHiGGsfnQZgDAxIqJYc+fOli4m7Yd2Ya6tjr0y+9neNkIIYTYCOlEHz5cfe6YY8RU8q++oohONMXS16YhInr/khKse+cd0QZCp5GHYOn3EgIz0UlSSBE9ZE0DAMDYowY2iuj6I2e2HHtseFSFrIMvvhBudaIfLS3AgQPi8VjVvIn8fHVgQ7YVoh9SRD+6nlgQKZzL/xNCIrOlVoy6Hj+g9wXdpIpJAIBNNZsMLBEhxtMd6MYXdaLTPq4s3CVS6i1FVWkVAODTQ58aXTRCCCF2o6cTHRAiOgBs22Z8eYjlqaqqSlpAtjzd3aooJReQ83rFvc8npu3rgBGfJUV0khRffinue4ro48aJe4ro+iPF2dGjw5+vqhJios8n3OpEP7ZvF/f9+onZeaHIeokQ40U0Ri5kLPtjify7vd3Y8hBiN6RwOLrf6F7/C4ro1ZsMLBEhxrPtyDZ0+juRn5uPYaXDev1/QrmYcvZpDUV0QgghcZAiek8nOkARnUQkI0V06ULPyVFd5263mDauKOr/NYYiOrEc8UT0PXuES5fohxTRx4wJf97lUgVcRrroi/z9I38PhSLrhU50fenuFjegtxNdiuh0ohMSmy/rRKc+qt+oXv8bP2A8HHCguqUaNS01RheNEMP47NBnAIBxZePgdPS+NJowQIjon9R8Ymi5CCGE2BA5XXnwYPW5Y48V9199ZXx5CDGDkCiXIA6H+rd0w9kQiugkYbq61IivniJ6376qI5cuaH2RdRBLwJWDHUQfEhHRs9WJHggEDDmO7Hdzc3tHqklRXceZYppi1GdGSCgBJRAU0Uf37+1EL3AX4Nh+4qLv45qPDS0bIUYiZ2SM7T824v+DTnTGuRBCCIlHdbW4r6hQn6uqEvd79hheHEJMIZKIDqgX6jo50Y2AC4uShDlwQMQaud3AwIG9/19VJRad3rmT62Xoiex7h/WecRycNcaBDH2JJaJLo4Ec7MgW3G43nE4nDhw4gLKyMrjdbjgcDt2O19Qk7nNzezvOpXAeCIiZMbm5uhUjLRRFQWdnJw4fPgyn0wl3zx8ZhOjI/qb9aOtqQ44zB8NLh0fcZlLFJHxZ9yU2VW/CuSPPNbiEhBjDjnrRYY/sMzLi/8eXjwcgRPSAEojoVrcLih1Glm0CP0tCSC+6uoDaWvE4VEQfMkTc79snLlR0vEYixBJ0dYn7nhfi8nqXIjrJBqR4O3hw+IKWkuHDgY0b1Rgwoj2KotbD0KG9/y8HuSmi64sUyEPXi5GE1kE2/UZyOp0YPnw4Dh48iANyGqOONDYCDQ1AQUHkz7i+Xgz65eT0jnuxGvn5+Rg6dCickU6shOiEdKGP6DMCua7II01ykcWttVk6tYZkBVJEH9EnQqcO4Ji+x8Cb40VbVxt21O/AMX0jjKBbnNyjF7FtbW3Ik6tvk7Roa2sDoH62hBCCQ4fEBaDLBfTvrz4vHYg+H1BXF/4/ohmc3WshZO6qxUR0Lb4jFNFJwuzdK+7lQGpPpAuaIrp+1NYK163DAQwa1Pv/rANj2LdP3EdqC0OGiPppbwcOHwYGDDC2bGbidrsxdOhQdHd3wy9X49aJ++4DnnsO+MEPgJ/8pPf/77gD2LABeOQR4Jvf1LUoaeFyuZCTk6Ora5+QSMRaVFQis9Kl4E5IJrKzQfxoGt4n8oyMHGcOxvQfg03Vm7C1dqstRXSXy4XS0lIcOnQIgBi8Zb+TGoqioK2tDYcOHUJpaSlcPTPlCCHZi4xyKS8Pdx263eK5mhohqlBE1xSjZ0STBJBTxRUlfNq4rJf2dkMXMNNyBjhFdJIwFNHNR7rQKyoiu2uz1QVtJIoC7N8vHoeuFyORcUf79wO7d2eXiA4ADocDubm5ujuzPvtMfL6lpepCoqEUFor/f/kl8J3v6FoUQmzJtiMil+rYvsdG3UYK7FJwJyTT6PJ3YU+j+HEVzYkOiAGlTdWb8FWdfReFqzgaLSCFdJIepaWlwc+UEEIARM5DlwweLET0ffuAE04wtlwZjtEzokkCVFeLmRcOh5rDCgjhvLZWTCk3AS1mgFNEJwkTK0YEoIhuBPHqQD7f0gIcOQL062dMubKJxkagtVU8jiSiAyKvfv9+MZhx8smGFS2rSHRQL9uy6QlJFCkcVpVWRd1GLixa21aLI+1H0DevrxFFI8Qw9jbtRUAJwJvjRUVhdEFUDjbZeVaGw+FAZWUlBgwYgC6ZVUpSIjc3lw50QkhvDh4U99FE9PXr1SnNRFOMnBFNEuCHPxSOtueeA447Tn1+zx7xv7w8MW3cQNenVjPAKaKThEnGiU4XtD7EE9Hz8kSfXV0tBFyK6Nojf/f06yc+70hUVQEffMBsej2R56NoAxkyr54iOiGRkSL60JIoHQqAQnchBhUNwv7m/fiy7kucOvhUo4pHiCHIPPSq0qqYC4YGo42O2FdEl7hcLgrAhBCiB/Gc6ABFdB0xakY0SYCNG4HmZqCsLHza+ODBQlwHRC56SYk55UsDrmJGEiaeiC6jRJqbhQuaaE88ER3gjAC9kb97oom3gNoWZP9AtKWjQ+TNA9HPR1JEZzsgJDJSRB9SEqURHWV0/6ORLrWMdCGZx8560UnEinIBVCe6neNcCCGE6Iy8QImU5ykvWqSoQkim0t4uREFArAUQSn4+UFwsHsuZGzaDIjpJmHgiunRBAxSu9CIRET00F51oTyIi+rBh4p51oA8yk97rBfpGSZeQbWTfPoALtRMSjq/bh5rWGgCxnegAMKovFxclmYt0og8vjbyoqEQ60fc27UVbV5vu5SKEEGJD6urEfaTp4HSik2xBrr3i8aiCeSgDB4p7iugkk2lrU/uEWAKuFNilyEW0RYroUqSNBEV0fUnGic460IfQAb1osVGVleK+s1OsXUIIUdnXJE5keTl56JcXO/dLOtEzIcaCkJ7saYq/NgAA9Mvvhz7ePgCA7Ue2610sQgghdoQiOiHqjIyyssgX6/JC3aaLwFJEJwkhRavCwtixRYMGiXuK6PogP1f5OUdC9s82PSdZnmSc6HLQg2iLrINos2IAwO1WZ1LyfERIOKFRLvEW15ExFzL2gpBMYn+T6CAGFcX4YXWUYC46Z2UQQgiJhMy0jSeiK4pxZSLEaGK1A0AV0elEj8xjjz2GqqoqeL1eTJkyBWvXro267VlnnQWHw9Hrdv755we3ufrqq3v9f8aMGXq/jaxHCrKDBsVeMJQiun4EAkCNmH0fca0SiZwdwzrQh0REdFkHzc1qHBjRjniLikp4PiIkMnubRCOKF+UCqA7dnQ0U0Unmsb/5qIheHF9EH9l3JAC2BUIIIVGQTvRIeZPyAr69HWhpMa5MhBiNFNGj5a5KscSmrk9dRfTnn38e8+bNw4IFC7BhwwZMnDgR06dPxyGZkdODF198EQcPHgzeNm/eDJfLhUsuuSRsuxkzZoRt97e//U3Pt0Ggirc91wXoic3bg6Wprwe6u8XjSGuVSFgH+pKIiF5UJG6AbQdYLY38TOV3PRoU0QmJjHSiDy2OL6LLrOgj7UfQ5GvStVyEGImiKDjQLH4sDSyK06EAqCqpAgDsatilY6kIIYTYllhxLgUF4gYA1dXGlYkQo4knotOJHp1HHnkE1113HWbPno1x48Zh0aJFyM/Px5IlSyJu37dvX1RUVARvb775JvLz83uJ6B6PJ2y7Pn366Pk2CBIX0Sla6Yfsa/v2FVEV0ZB1cPAgF1TUA3mul+f+aHAwQz8SPR/JgQ6ejwgJZ29j4k70Ik9RMDed4iHJJBp9jcFFQhMS0TkrgxBCSDS6u4GGBvE4WoyFdKPLixlCMhGK6KnR2dmJ9evXY9q0aerBnE5MmzYNq1evTmgfTz75JC6//HIUyBG7o6xatQoDBgzA6NGjMWfOHNTJET+iG4nEiAAU0fUkUeGwvFxE7vj96poORBu6utQ+gbMyzENOZuKgHiGpIeNcBhfHyUQ6SlA8ZC46ySCkC73UW4r83Py428t2wMEkQgghvaivVx9HEw/lxQtFdJLJMM4lNWpra+H3+1HeQ+UoLy9HdQLTV9auXYvNmzfj2muvDXt+xowZeOaZZ7By5Uo8+OCDePvtt3HeeefB7/dH3ZfP50NTU1PYjSRHsnEuFK20RzabeAMZOTlqPbEetEUOSrhc0fsEic37BkvDmTGEpMfBFuH8SMR9CwDD+4hIF4qHJJNIZlFRIFxEV7goHCGEkFCkcFhSIi7IIyEvXhjnQjKZDHeiR2nd5vPkk09i/PjxOOWUU8Kev/zyy4OPx48fjwkTJmDkyJFYtWoVzjnnnIj7WrhwIe655x5dy5vpJCtaNTYCra1q7BdJn0TrABACbnW1EHBPPFHfcmUT0gFdVgY44wxBUkTXD4rohKRHdYu4eKsojDMqexSZi84YC5JJSCd6IouKAmr8UVtXG2rbalFWUKZb2QghhNiMWHnoEsa5kGwgURG9pQVoblYXk7MJujnR+/fvD5fLhZoeJ4iamhpUxLHStra2YtmyZbjmmmviHmfEiBHo378/tm3bFnWb+fPno7GxMXjbu3dvYm+CBElUtCouVoVziofakqgTHVDFQ9aBtkgRPdbCrhKK6PoQGqkTrx5kO5CLwRJCAH/Aj0Ot4mSWqIjOLOjkqWmpwZ3/dyee++Q5upYtyv5mMcKa6IwMT44nuC1nZSTOy1tfxs9W/oyfGSEks4knHAKMcyHZgWwL0dauLCwE8vLEYxvmD+smorvdbkyePBkrV64MPhcIBLBy5UpMnTo15mtfeOEF+Hw+XHXVVXGPs2/fPtTV1aEyxip/Ho8HxcXFYTeSHImK6A4H3Z96kawTHaCAqzWyDiiim0dopE4sowegnosaGoC2Nl2LRYhtqG2rRUAJwAFHwk5a6USnCJYY/oAf05+djvvfvR9XvXQVntz4pNlFIhFINs4FYC56svxr67/w7ee/jYXvLcTZT5+N9q52s4tECCH6IBcVLS2Nvg1FdJINxBtQcjjE1H5AdSnaCN1EdACYN28eFi9ejKeffhpbtmzBnDlz0NraitmzZwMAZs2ahfnz5/d63ZNPPomZM2eiXw+FpKWlBbfccgs+/PBD7Nq1CytXrsRFF12EY445BtOnT9fzrWQ1ipKagEsRXVuScaKzDvQh0QUtAYroeiHPRYlE6pSUqIPcjB4kRCCjXAYUDECOM7FUP5mJvrN+J13VCfC/X/4vPq75OPj3L975BfyB6Gv3EHM40CI66ESd6ABF9GS5++27g493NezC0x8/bV5hCCFETxobxX1JSfRt5IU8L0xIJpPIrAzpSqQTPZzLLrsMv/71r3HXXXdh0qRJ2LRpE5YvXx5cbHTPnj042CNM/osvvsB7770XMcrF5XLhk08+wYUXXohRo0bhmmuuweTJk/Huu+/C4/Ho+VaymoYGoLNTPE5EPJSTAjjAqi0cyDCfVJ3o1Jy0I5l24HCodWXDQW7Teeyxx1BVVQWv14spU6Zg7dq1Ubc966yz4HA4et3OP//84DZXX311r//PmDHDiLdCQkg2Dx0AhpUMAwA0dzbjSPsRXcqVSbzw+QsAgBtOugHFnmLsbtyNDQc3mFwq0hOZiZ6UiF5SBYAieiJsO7INm6o3IceZg5+d/jMAwLLNy0wuFSGE6EQiIjqd6CTTURSgvl48jhbnAtjaia77wqJz587F3LlzI/5v1apVvZ4bPXp0VJdTXl4eXn/9dS2LRxJAnuNLSgCvN/727Bv0IRknOgcy9CGZTHRZB+3tYiAqVh9CEieZOgDE+Wj3braFZHn++ecxb948LFq0CFOmTMGjjz6K6dOn44svvsCACB/+iy++iE452gqgrq4OEydOxCWXXBK23YwZM/DUU08F/+YAuPEcbBHmhWRE9LzcPFQUVqC6pRo7G3aiX36cLKUsptPfiX9/+W8AwFUTrsLBloN4aetLeHPHmzh50Mkml46EkuzaAECIE71xlw4lyiyWb1sOADh96On4/gnfxwPvPYDV+1ajvasdebl5JpeOEEI0JlkRXVGE44eQTMLnUx24saKN6EQnmUwyzk+Azk89CASSixJhHehDMnWQl6f+hqKAqx3Jno84qJcajzzyCK677jrMnj0b48aNw6JFi5Cfn48lS5ZE3L5v376oqKgI3t58803k5+f3EtE9Hk/Ydn04umQ4qTjRAWBoyVAAwL4mrtQbi4+rP0aTrwl98/piyuApmDZiGgDgzR1vmlwy0hMpog8oSHBUFoxzSYbXtwvj03nHnIcRfUagsrASnf5OrN0ffVYTIYTYlmRE9PZ2oKVF/zIRYjRNTerjwsLo20knOkV0kolQtDKf+nohpANA//7xtw8V0Rkloh3JxLmEbsfBDO1IdVCP56PE6ezsxPr16zFt2rTgc06nE9OmTcPq1asT2seTTz6Jyy+/HAUFBWHPr1q1CgMGDMDo0aMxZ84c1NXVRd2Hz+dDU1NT2I2kT6oi+uDiwQCAvY17NS9TJiEFwimDpsDpcOKsqrOCzweUgIklI6G0drairUusOJ2qiM71AaKjKArW7FsDADhz2JlwOBw4Y9gZAIB397xrZtEIIUQfEhHRCwrEDeDFCclMZDsoKoq9gJmN41woopO41NaKe/k9jweFQ+2RdVBcDLjd8beXddDZqZ7HSPokGyXCtqA9ycwGCN2OdZA4tbW18Pv9wfVLJOXl5ahOYCGktWvXYvPmzbj22mvDnp8xYwaeeeYZrFy5Eg8++CDefvttnHfeefD7Iy+4uHDhQpSUlARvQ4YMSf1NkSCpiuhDisXnTyd6bNYeECL6KYNOAQCM6jcK3hwv2rrasP3IdjOLRkKQLvS8nDwU5BbE2VpFDia1dbWhoaNBj6JlBAeaD+Bw22G4HC5MKJ8AAJg6eCoAcH0AQkhmIi+6i4tjbydFFXmBT0gmIU1PsQaTAMa5kMxGnt8TcUADdKLrgTRr9kswhjYvTwz+ARQPtUJRkhdwKaJrj/wsEx3U4/nIeJ588kmMHz8ep5xyStjzl19+OS688EKMHz8eM2fOxL///W989NFHEddHAYD58+ejsbExeNu7lw5oLUjbid7EeojFR/s/AqCK6DnOHBw/4HgAwCc1n5hWLhJOaJSLI4lM2rzcPPTLEz/GOKAUHSmUjy0bG8w/l+3gs8OfmVYuQgjRjUSc6ICtYywIiYsU0RMdTLKhUEIRncQlWQGXUSLak+xABkABV2uamtQ1Mjgrwzzk+SjRtsA4l+Tp378/XC4Xanp8aDU1NaiIs7Jxa2srli1bhmuuuSbucUaMGIH+/ftj27ZtEf/v8XhQXFwcdiPpQye6fnT6O/Fl3ZcAgInlE4PPTxggnLgU0a1DKnnoEjmgxLYQHSmin1h5YvC5cWXjAADbjmxDR3eHKeUihBDdSNSBKy9i6EQnmUiiMzLoRCeZTKoiOqNEtCPZOgDowNUaWQd5eeKWCBTRtSfZtsA4l+Rxu92YPHkyVq5cGXwuEAhg5cqVmDp1aszXvvDCC/D5fLjqqqviHmffvn2oq6tDZWVl2mUmiSPFw7L8BEcDj0Ineny2HdkGv+JHkbsIA4sGBp8fXz4eALD58GazikZ6QBFdX7bUbgEAHF92fPC5ysJKlHpLEVACwcEmQgjJGBJ1olNEJ5lMooNJoTMybOa8pYhO4iLP74wSMQ860c0nlYEM1oH2pCqiczApOebNm4fFixfj6aefxpYtWzBnzhy0trZi9uzZAIBZs2Zh/vz5vV735JNPYubMmejXo4JaWlpwyy234MMPP8SuXbuwcuVKXHTRRTjmmGMwffp0Q94TAboD3ajvqAcAlBWkJqLvb9rPBTKjsOWwEA7H9B8TFhFybN9jAQiRnViDmlbRKZQXJJjPFgJF9PhIkXx0/9HB5xwOB44rOw4A8NkhRroQQjIMxrkQknyci88HNDfrWyaNyTG7AMT6JBufAAjxsLlZCFejRulTrmwi1ToAKOBqBUV08+nqUvvlZGfG1NeL2TGJLMxLgMsuuwyHDx/GXXfdherqakyaNAnLly8PLja6Z88eOHusuP7FF1/gvffewxtvvNFrfy6XC5988gmefvppNDQ0YODAgTj33HNx3333wePxGPKeCHCk/Ujwcd+8vkm9dmDRQDjgQFegC4dbD6O8MHnxMdOR7tsx/ceEPT+y70gAwPYj26EoSlIZ3EQf6ETXD0VRgiL6qH7hFwHH9jsW7+99Hzvqd5hRNEII0YdAQBUC6UQn2UyiInpBAZCfD7S1iQElG8V2UkQncUk1SmT7doqHWpHsbACAMRZaQxHdfI4c1f8cDqC0NLHX9O0LuFyA3y/qYfBg3YqXccydOxdz586N+L9Ii4GOHj0aSpTpeHl5eXj99de1LB5Jgdo20Zn08fZBjjO5n4C5rlxUFFbgYMtB7G3aSxE9Al/UfQGgt4g+vHQ4HHCgtasVh1oP8bOzAJqI6M0U0SNxoPkAWrta4XK4MKLPiLD/DS8dDgDY2bDTjKIRQog+NDerkRQU0Uk2k+iMDEC40XfvFhfpI0fqWy4NYZwLiUs64iEjFLQhHSc660AbpIBLEd08ZDvo00cI44ngdLIeCJFIEb1/fhKdSQhDSri4aCx21gthcGSf8AsBT44nKLxur99ueLlIb+hE1w/pQh/eZzjcrvDpX1JEpxOdEJJRSOHQ7Qa83tjbMs6FZDKJOtEB2y4uShGdxKSzM/n4BIAuaK1JxYlO4VBbpIDbN4kEhJ5RIiQ9UhnQA5iLTogkXRE9uLhoIxcXjcTuxt0AgGGlw3r9LzTShZiPFiI620FkpMu852ASIIT10G0IISQjSMZ9Syc6yWSSEdFtOqBEEZ3ERLpvnc7E4xMAuqC1hpno5pOKgNu3r2g7AH8naUEqg0kAz0eESNJ2ohfTiR6NTn8nDjQfAABUlVb1+v+IUhFrQfHQGmghojd3NqPJ16RpuTKBPY17AADDSnoPJkkn+t7GvegOdBtaLkII0Y1URHSbCYeEJEQybcGmghVFdBKTVOITADrRtUaKh8mI6HTfaksqIrrTqQ6wsi2kT7pOdNYByXY0c6I30YHbk31N+xBQAvC4PBGFWRmFs79pv9FFIz1QFCWttlDoLkSptxQAB5QiIUX0oSVDe/2vsqgSHpcHfsVPJz8hJHNINgdavqarS78yEWIGdKKTbIfOT/MJBNLLpW9oYJSIFlDANR/GuRCSHlqJ6BQOe7O7QY1ycTp6/7zmYpTWocnXBL/iBwD0y0+yQzkK20J0YonoToeTnx0hJPOQInoiwmFpqTpVWV7cEJIpUEQn2Q6FQ/NpbBRCOpBcPYTOHrDZecmSpLKwKGDbWUqWJNXzEQf1CBFoFedCJ3pvgnnoESIsAIquVqKuXXQm+bn58ObEWQAuCqzP6MQS0QFgUPEgAMD+Zs7KIIRkCMk40V0udZEtXqSTTCOZtiAv6m02mEQRncQklSxugKKVlsjZAIWFgMeT+OucTtaDlqQr4FJET59U60Cev2zWPxOiOVo50fc37UdACWhWrkxgV8MuAJHz0AEuRmkljrSLUfF+eam50AFgcBFF9EgoihJfRC86KqIz2ogQkilI920iwiGgOnC5aBbJNJJxolNEJ5lIqqKV7Beamhj1lS7SAS0HrJOBIrp2yLaQbD1QRNeOdEV0/k4l2Y504KYqog8sGggHHOgKdOFwK91TocRzoksXf31HPVo7Ww0rF+lNXZtoB33zUvhhdRQ60SNzuO0wfH4fHHAEHec9GVg0EACd6ISQDKK5WdwXFSW2PS9OSCaiKBTRCUlVtCotBRyO8H2Q1KivF/d9+iT/WjmYwTpIj64utT+gE9086EQnJD3SdaLnunJRUVgBgJEuPZGZ6NGc6MWeYhS6CwFQPDQbOZiUah46QBE9GtKFXllUCbfLHXGboBOd7YAQkim0tIj7wsLEtpcXJ4xzIZlEezvQ3S0eM86FZCupCrihUV82axOWo6FB3KciosvzEge500POBnA4kq8H+RuJInr6pCqisx0QIkhXRAdUF+nB5oOalClTkIMKQ0qGRPy/w+FgjIVF0CTORcbzcDApjHhRLkBIJjrbASEkU0hWRGecC8lEpOvQ4QAKCuJvLy/SGxpU8d0GUEQnMZECbmlp8q+lcKUN6TjRbTq4ZzmkiF5aqi7Wmih0QWuHbAvJRurIOmhuBjo7tS0TIXah09+JJp/4cauFiH6g+YAm5coU5KBCZWFl1G3KC8Wq64daOapqJlrGuVAIDichEZ1OdEJIpiFF9GTjXOhEJ5mEFNGLitRYiliEXtRL4dEGUEQnMUlHwGXUlzakM5DBOtCGdHLpOZChHanOyigtFQvtAqwHkr1I4dDpcKLUW5ryfiii96bZ14zWLpFzXlkUQ0QvECJ6TSsXKjGTYJxLGk502Q7qO+rR0d2hSbkyAXlekEJ5JEJnsyiKYki5CCFEVxjnQkjyg0k5OWp2uo0u0imik5ho4US3UXuwJHSim48WkTqsg/To6BA3IPnzkdOpDoBwQIlkKzLKpV9ePzgdqf/8CwpgLYxzkcjPotBdGMw9j8SAArFIRk0LRXQzkXEu6TjRS72l8Lg8ABhtFIpsC7FmZMh24PP70NLZYki5CCFEV1IV0aVTi5BMoFUYShJuB4AtxRKK6CQmdKKbjxZ1YKNzkiXRYjZAfT3g92tVouxD1oHDkfjgdihsCyTb0SIPHaATPRKJRLkAqhOdcS7mosXCog6Hg20hAtUt1QAQXIA4EgXuAhTkiqxUtgVCSEaQrIjOxeNIJpJsOwAoopPMg05082EuvfnIgYxU6kD+RlIUdT8keULbgTOFnouDeiTboYiuH0H3bYwoF0DNRGeci7nIaKN04lwAtoVIJCKiA6obnSI6ISQjSFY8lBfpdKKTTEK2g0QWFZXYUDSkiE6iEgikF2NB0UobGOdiPukMZOTm2jLqy3KkM5ABcECJkGCcSxruW4DCYSSkcJioE50iurlIJ3o6cS4A20Ikgm0hzoASRXRCSEZBJzohdKIT0tws3LNAejEWNmoPlkSrSB2u3ZQ66QwmARxQ0oJ0BjIAno8I0WIxRUAVDg+1HkKXvyvtcmUCCce5SCc6M9FNRWaiazWgxPUBBF3+ruBgHZ3ohJCsIlUnenMz0MXfUiRDoIhOsh0p3nq94pYsdH5qgxZxLj4f0NamVYmyj3QFXBv2DZZDKxGd5yOSrdS3i069jzfF0cCj9M/vjxxnDhQodFQfJdE4l1DhUOHItil0B7rR0NEAIP0BJTloQie6QJ4Pcpw5cV3+FNEJIRmDoiQvHpaWioWeAEa6kMyBC4uSbIfOT2uQjhO9sBBwu8Vj1kPqUEQ3n3TaAcBBPULqO46K6HnpiehOhzPoMqV4KAiK6AnGubR3t6Ols0X3cpHeyMEkIP22wDiXcGSUS3lBOZyO2JeYFNEJIRlDRwfg94vHiYqHLpd6YUkRnWQK6WSi26gdUEQnUaFoZT6BANDYKB6nUg8OB+tBC+iCNh8O6hGSHkERPU0nOkDxsCfBOJc4TvQCdwEKcsWFBV385iCjXEo8Jchx5qS1L7aDcBJdVBQIEdHbKKIbzWOPPYaqqip4vV5MmTIFa9eujbn9o48+itGjRyMvLw9DhgzBT37yE3R0dBhUWkJsQEvIoHgy4qHMRbeReEhITBjnQrIdrUSrhgagu1uDAmUhzc1CSAfogjYTrRa1ZB2kDgcyCEmPYJxLmu5bgOJhTxJ1ogPMRTeb4NoAaeahA2wHPUl0UVFAFdEPtx7WtUwknOeffx7z5s3DggULsGHDBkycOBHTp0/HoUORBzP++te/4vbbb8eCBQuwZcsWPPnkk3j++efxs5/9zOCSE2JhpHCYlycc5onCC0SSaaQiottwkV2K6CQq6S6mGPo6DrCmRrq59AAduFrAOBfz0WpxV9YByVZkDrQmTvTCowsqNnNBxS5/V9DdLAXyWMhIF8ZYmENdm+gE4mV2J4IUixt9jWjr4sIzQSd6QRJOdLYDQ3nkkUdw3XXXYfbs2Rg3bhwWLVqE/Px8LFmyJOL2H3zwAb72ta/hyiuvRFVVFc4991xcccUVcd3rhGQVUjgsKkrudXSik0yDmegk20nXfZuTowpeNmoTliJd4RBgnIsWaCXgsg5SR6vZAKwDkq1olYkO0IEbinQ2Ox3OhIRZiofmIgc80l1UFBCRMHk5eQA4oASon0FScS5sB4bR2dmJ9evXY9q0acHnnE4npk2bhtWrV0d8zWmnnYb169cHRfMdO3bgtddewze/+U1DykyILUjFfQvYUjwkJCbpZKLbqB2kFwZIMhqtBNz6egpXqZKucAjQgZsuikInuhXQKs6lqQno7FQX3CUkWwjGuWiZid5CEV3GUfTL6xd3MUW5HaCK78RYtIxzcTgcGFg0ENvrt+NA8wGM7Dsy7X3aGZnzn8iMjLL8MgDA4bbDCCiBhNoOSY/a2lr4/X6Ul4fXT3l5ObZu3RrxNVdeeSVqa2tx+umnQ1EUdHd344c//GHMOBefzwefzxf8u6mpSZs3QIhVSVVEpxOdZBrpZKJ3dABtbUB+vvbl0hj+YiFR0VLApYieGuku7grQgZsuLS3MpbcC6YropaWA82iPx9+qJNvoDnSjubMZAJ3oWnO4TYjo/fP7J7S9FG9lrAgxFulE7+tNP84FYFsIpbZN/NCUAnksZHsJKAG2BQuzatUqPPDAA/jjH/+IDRs24MUXX8Srr76K++67L+prFi5ciJKSkuBtyJAhBpaYEBOgE50QQSptoahIRFgAtmkLFNFJVLRwotMFnR7pCocA++d0kXXgdjOX3kzSHVByOlXDBweUSLYh89ABoNRbmvb+KByqBIXDgvjCIaBmcR/p4GieGQTXBtBgMAlQ24JcXDabkW0hkQGlXFdu8FwkBzaIvvTv3x8ulws1NeGLGtfU1KCiInIEz89//nN897vfxbXXXovx48fj29/+Nh544AEsXLgQAekw6cH8+fPR2NgYvO3du1fz90KIpaATnRBBKpnoDoftBCuK6CQqWjjR6YJODy2c6BRw0yO0HTgcqe0jtF9QFE2KlXVoMaDEtkDSwR/w43+/+F98tP8js4uSNDLKpchdhBxn+kl+UjisbauFr9sXZ+vMRsa5JOK+BULiXGzsvt1RvwMvfPYCGjsazS5K0kgRXYvBJACoLBSLi3JAKfUBJUYbGYPb7cbkyZOxcuXK4HOBQAArV67E1KlTI76mra0NTme4XOByuQAASpQftB6PB8XFxWE3QjKadEV0XpiQTCGVTHRAFUtsMqBEEZ1EhU5082Gci/loORugu1tkcpPk0CKXHmC8FEmP6/73Oly47EKc8udT8OKWF80uTlLIRUW1Eg775vWF2yUWFqhuqdZkn3YlGfctEBLnYlPh8Mu6LzFp0SRc+o9LcebSM203iKK1iM5ZGQJFUZJuC8FZGXSiG8a8efOwePFiPP3009iyZQvmzJmD1tZWzJ49GwAwa9YszJ8/P7j9BRdcgMcffxzLli3Dzp078eabb+LnP/85LrjggqCYTkjWk26ci02EQ0LikiXRRlxYlESFTnTzofvWfLQYTMrLE2tktLWJeigp0aRoWUNLC+D3i8c8HxEz2HxoM57a9FTw71vevAUzx8y0zWJ4wUVFNYqwkAsq7mrYhQPNBzCsdJgm+7UjMhM9USd60H1rUyf6wx88HMzX/7jmYzz7ybO45sRrTC5V4lBE14eGjgb4FdFRy9kW8ZDbUUQ3jssuuwyHDx/GXXfdherqakyaNAnLly8PLja6Z8+eMOf5nXfeCYfDgTvvvBP79+9HWVkZLrjgAtx///1mvQVCrAed6IQIsqQt2OPqj5gCnejmQye6+WgxkAGwHtJB1kFubnoLdvN8RFLluU+eAwBMHzkdxZ5i7KjfgdV7V5tcqsSRTvQ+Xm1EdIDioSRpJ7qNhcOWzhb8dfNfAQDnHXMeAOBvm/9mZpGShiK6Psh2UOQugifHk9Br6EQ3h7lz52L37t3w+XxYs2YNpkyZEvzfqlWrsHTp0uDfOTk5WLBgAbZt24b29nbs2bMHjz32GErT/VFMSCZBJzohQGcn0NUlHme4E50iOokKnejmo8VAhqyD1lbAZ69Z15ZAaxHdJn2DpQitg1Rz6QHGuZDUeXPHmwCAqyZchW8e+00AwIodK8wsUlJo7UQHKB5Kgk70ZBcWbT8SNVPYqry96220dLZgeOlwPHzuwwCAd/e8i9bOVpNLljhaRxtxYVFBsoNJAEV0QkiGkK77lhfpJBNoDfktmGomuk2EEoroJCI+H9DeLh7TiW4eUjxMJ/6jpASQsYWsh+TRSkRnW0gdLQb0APW3Kg0fJBnq2uqw4eAGAMA5w8/BWcPOAgC8vfttE0uVHLo40QspHgIpLCx6NBPdr/jR6LPXwpzv7nkXAHB21dkY038MhpYMRae/01ZtQfOFRYvEwqJNvia0dLZosk87ko6IbtdoI0IIAZD6YoolJYCMT+LFCbE7sh243WL6eDJQRCeZQOPR6zqHA0hnUXU60dNDLkKZjojudKriIesheRjnYj5azMgAKKKT1Hh/7/tQoGBs/7GoLKrEmVVnAgA+2PuBbRZVDDrRGeeiOcmKh94cL/JzRS6V3Ry47+x+BwBwxrAz4HA48F/D/wsAsGbfGjOLlTCd/k60dbUB0E5EL3IXoSBXCCcHm7N3QCktJ3qHvdoBIYSEIR24yYroTqd6cWMT8ZCQqKQ6IwOgiE4yAymiFxWpA6SpIEWrhgYgEEi7WFmHFNHTGcgAbHdeshRauaBZB6mj1UAGRXSSCpuqNwEATh50MgBgdL/R6OPtA5/fhy21W0wsWeIEnegaxrlIB242i+iKogTFw0TjXAB7OnC7/F1Yf3A9AOD0oacDAE6sOBEAsLF6o2nlSobGDtX5X+LRZoVvucgukN1tgXEuhJCspU0MziYtogPMRSeZA0V0ku1oJd7KwdVAQN0nSRw5mJGOEx1g/5wOWrmgmcedOlrEGgEU0UlqfFzzMQBgYvlEAEI0G18+HgDwac2nppUrGbiwqD40+ZrQFRCLKCUjHsrFReva7XGxAADbjmxDp78The5CjOgzAgAwqWISAHWgyerIKJdiTzFcTpdm+2UuOkV0QkgWI0X0/PzkXysvTmwiHhISlVRnZAAU0Xvy2GOPoaqqCl6vF1OmTMHatWujbrt06VI4HI6wm9frDdtGURTcddddqKysRF5eHqZNm4avvvpK77eRdWgl3nq9an9C4So5FEW7wQyKh6mjdZwL6yB5tIg1AtgOSGp8XB0uogPA+AFCRN98aLMpZUoWLiyqD3JR0UJ3Ibw53jhbq8hcdDs50eV3/biy4+B0iMuHiRWiText2muL96J1HrqEbYEiOiEki0lHROcFIskUUl0bAKCIHsrzzz+PefPmYcGCBdiwYQMmTpyI6dOn49ChQ1FfU1xcjIMHDwZvu3fvDvv/Qw89hN/97ndYtGgR1qxZg4KCAkyfPh0dHR16vpWsQyvxFlCFKxmLQRKjtVUI6YB2IjrrIHnkgBIHMsxDj8Ek2bYIiUVrZyu2128HoAqGAHD8gOMBAJ8eohO9vqMe7V3tmu3XTiS7qKjEjuLhZ4c/AyBEdEmxpxhVpVVh/7cyeonolYWMNpIDSlxYlBCSddCJTojqRE8lzsVmGdC6iuiPPPIIrrvuOsyePRvjxo3DokWLkJ+fjyVLlkR9jcPhQEVFRfBWXl4e/J+iKHj00Udx55134qKLLsKECRPwzDPP4MCBA3j55Zf1fCtZh5YiuozBoHiYHFK8zckB8vLS2xfrIHW0ckHbbIDVUmjtRO/qUvt5QmIhBfR+ef3CxCEpottBOAT0caKXeEqQlyM6p2yNsUjFfQvYM85FOtHld18yqt8oACLuxerQia4fwbUBkhhQku2g0deI7kC3LuUihBDdkRcVdKKTbCadwSSbZUDrJqJ3dnZi/fr1mDZtmnowpxPTpk3D6tWro76upaUFw4YNw5AhQ3DRRRfhs8/UC9SdO3eiuro6bJ8lJSWYMmVKzH36fD40NTWF3Uhs9HCis29IjtA6cDjS2xfrIHUYqWM+WtVBXh7g8YjHrIf4MI4N2FG/AwCCGdCSY/seCwDY27gXnf5Ow8uVLHo40UMXVNzftF+z/doJ6b5NZlFRwJ4OXCmSj+4/Ouz5Y/ocAwD4qs7abRlQ2wFFdO1JZUApdFBPDnAQQojtSGdhUTrRSaaQjohuswxo3UT02tpa+P3+MCc5AJSXl6O6ujria0aPHo0lS5bgX//6F5599lkEAgGcdtpp2LdvHwAEX5fMPgFg4cKFKCkpCd6GDBmSzlvLCrSKsAAoHqYKBzKsgdYiOn8jJY9W5yOHQx3oZrRRbBjHJogmog8oGICC3AIoULCrYZcJJUscf8CPJp84keklHtKJnpwTXdZDo69R6yLpgqIo2NmwEwAwvHR42P+O7ScGlLbV04mere0ASK0t5DhzUOwRHbudoo0IISQMZqITkl47AGx1ka77wqLJMHXqVMyaNQuTJk3CmWeeiRdffBFlZWX405/+lNZ+58+fj8bGxuBt7969GpU4c9EqPgGggJsqWi3uCrAOUsXnEzcgfQFX/kZqaQE6rW9ctRQcUDIexrEJth8RcS4j+4wMe97hcASFdSm0W5Xmzubg4xKvBh1KCNnuwA0Kh3mpieh2cd/Wd9QHB2JkBrpEzsqwgxNdft5azsgAgMqi7M5E7w50B13+yQ4o2XF9AEIICRIIANIMkk4mOi9MiN1JV0S3UVvQTUTv378/XC4Xampqwp6vqalBRUVFQvvIzc3FCSecgG3bhLtFvi7ZfXo8HhQXF4fdSGwoWpkP68B8mlXtCUVF6e2rpESN5bHBAKulYFswFivFsZnNjobITvTQ56TQblUaO8SIrNvlhjfHG2fr5Mh2EV0Kf/3y+yX1Ors50XfWCxd6RWEF8nLDF2k5pq+Ic9l2ZBsUi6/YrPfCoi2dLWj2NcfZOvOQ7cABR9LrLsgBDTtFGxFCSJD2kIXV03Gic6oysTt0oqeP2+3G5MmTsXLlyuBzgUAAK1euxNSpUxPah9/vx6efforKSvHjdPjw4aioqAjbZ1NTE9asWZPwPkliULQyHz3qwAbnJEsh66CgAHC50tuXy6X2DfydlBw8HxmLVeLYrLCeiRTII4no0p0uFx+1KlKoLfFo60IHKKKnmjVvNye6jHLp6UIHgKElQwEArV2twc/Dquglohd5ilDkFiPt2dgW5IyMPnl9kOPMSeq1dhtQIoSQMKRwCIjFl5KFFyYkU6ATXRvmzZuHxYsX4+mnn8aWLVswZ84ctLa2Yvbs2QCAWbNmYf78+cHt7733XrzxxhvYsWMHNmzYgKuuugq7d+/GtddeC0BMn7755pvxi1/8Aq+88go+/fRTzJo1CwMHDsTMmTP1fCtZhx6Z6BRwk0NL4VCKtzY4J1kKLesAsFXfYCkoolsfPeLYzF7PxB/wB/POR/Yd2ev/w/uIbGirZ6LLGA6to1wAiujSgSsjKRJFDmjYRUSX3/GeeegAkJebh7J8sbDqnsY9RhYrafQS0YHszkWXLvJ+ecnNyADU85KcMUMIIbaitVXc5+UBzhSkNTrRSaaQRSJ6cnaBJLnssstw+PBh3HXXXaiursakSZOwfPnyoBNtz549cIacbOrr63Hdddehuroaffr0weTJk/HBBx9g3LhxwW1uvfVWtLa24vrrr0dDQwNOP/10LF++HF6vtlOUsx0tM9Ep4KaGHpnoMo/b7U5/n9mA1iJ6v37Atm38nZQMisI1GoxG7zg2ObtM/j1p0qSI+5g/fz7mzZsX/LupqclQIX1f0z50BbqQ68zFoKJBvf4vn9vfvN+wMqWCFKf0cKLLzyBbRfT69qNO9CQjLILuW5sIhzLOJZKIDgg3+uG2w9jTuAeTKiYZWLLk0FtE/6LuC+xvsvb5QA/kYFKy7QCgE50QYnO0Eg47OsS+Ut0PIWbDOBftmDt3Lnbv3g2fz4c1a9ZgypQpwf+tWrUKS5cuDf79m9/8JrhtdXU1Xn31VZxwwglh+3M4HLj33ntRXV2Njo4OrFixAqNGjdL7bWQddH6aj5Z1wDzu1KAT3Xw6OoDubvGY5yNjsEocm9nrmUj37bDSYXA5e+c5DSo+KqJbXDQLxrnQia45QfEwxTiXJl8TAkpA62JpjoxzkbMveiIjXfY27jWsTKlAJ7o+pBprBNhvVgYhhISRrnBYVKRmhvIindiZLHKi6y6iE3tCEd18tKwDlwsoLRWPWQ+Jo5eITid64sg6cDhENn268HyUGIxjUx3mg4sHR/y/dGFXt1TDH/AbVq5kkW7nYo/2gxCVRWKQpLmzOSsXVJTiYdJxLkcHNBQowbgdKxMrEx0AhhSLGSJWj3OR9aWniG71QTU9SHVGBmC/WRmEEBJGusKhw2ErBy4hUckiEV3XOBdiX/TIRD9yREQzSEc0iY2WcS6AqIf6eluclyyDlu0AUGPvWAeJI+ugqCi1qMGeMF4qMRjHprqrpTjWk4rCCrgcLvgVP2paa6JuZzZ6Lixa6C5EsacYTb4mHGw5iCJPkebHsCpd/i60dLYASF489OZ44XF54PP70NDRoIuoqyXSYS4d5z0JOtGb6EQ/0JJ9szKCg0ne5AaTgBAnuq9ByyIRQogxpCscAuLipLaWFyfE3mRRnAtFdNKL0AxiLUV0nw9ob2fUV6Lo4YLevt0W5yXLoGUWN2CrAVbLoNdsALaD+MydOxdz586N+L9Vq1aF/f2b3/wGv/nNb2LuT8ax3XvvvVoVUVekozRSHjoAuJwuVBRWYH/zfuxv2m9ZET24sKgOIjogxMMmXxMONB/AqH7ZE68nhUMgtc+21FuKmtYayztwm33NaO0SC6dF+44PKRFOdCuL6L5uHzq6OwCkFjsSj2yONqITnRCStciFRdOZLsuLE5IJZJETnXEupBehGcRaiIeFhUDO0eEa9g2Jo7V4SAdu8uixsCjAOJdkYC49MQvpKI0ljgdz0S28uGhwYVEdMtGB7I2xkMJhqbc0YmZ+PKR4aPUsaJnxXeQuQqG7MOI2lYUi1qe6pdqwciVL6MKVesyYyOZFdtPKRPcyE50QYmO0cqIDvDgh9kYrEd0GgiFFdNILrTOIQ6O+2Dckjh5xLgDrIBko4JoPZwMQs4jnRA/9376mfYaUKRX0jHMBsteBm+qiohK7iOiyXmX+fSQqCisAWFtElzMyitxFcDq0v/wJHUxSFEXz/VuZoIieghNdnpdCBzkIIcQ2aCGi20g8JCQq7e3iPt04FxtcpFNEJ73QOoMYoHCVChRwzYcLi5qPXnXQ2ioipgiJRrxMdEAV0a3swg6K6Ho50QuzU0RPRzgE7OPAPdgsnOix2oEU0Vs6W4I58VZD7xkZcpDB5/eFRf1kA+kMKDHOhRBia+hEJ0SglRO9rc3yF+kU0UkvtBatAAq4qUAR3Xz0inNhHSSO1nVQXKwODtLwQaKhKEpQFJaRLZEYXDwYgD3iXIo9GnbqIWTrgooyzqVvXvKLKQIh4qHFHbhBJ3phdCd6kacIBbli6qJV3ejSia5XO/DmeNEvT3TyWTeglEYmul0GkwghJCJ0ohMiSLctFBeLCAvA8m2BIjrphdbxCQAF3GQJBIDmZvGYMRbmQSe6+WhdB04nDR8kPkfaj8DnFy6IWOKhHTLRjVhYFMg+4TDtOBdPKQDri4cyEz3ewrlWj3TRW0QHsnh9gDQy0eVgUmtXK7oD3VoWixBC9EeLhUXlhYnFhUNCohIIpB/nYqOLdIropBd0optPSwsgIzW1FnDZPyeOXk70tjaxgC+JD89HxAykINw/vz88OZ6o29lBQNY9zsUGn4EeSOEwXSe61UX0RJzogPVFdNkOjBDRs6ktKIqSnhM9ZHCPkS6EENuhpROdFybEroQKG1nQFiiik17ITHQ9RCsKuIkhhcPcXMATXb9JCpsM7FkKRomYjx7nI7YFEg/pLI/nvh1QMAAAcLj1sO5lSpVgFrQBTvRsWlAxKBym6ES3S4xFpjnR9WoHQHaK6G1dbegKdAFIrS3kunKRnysuuK0ebUQIIb3QMhOdF4fErsh2AAB5eanvxyZtgSI66QWdn+YjhcOSEjUaKl1YB8mjR5QII12Sg/FSxAwSWVQUUEX0uvY6S0YRKIqiioc6L6jY0d1heUFYS450HI1zSXFhUbtkosuFRWU9R0M61eX2VsOIOBe50HA2iehyRkaOMweF7sKU9iEHNrLp/EEIyRDoRCdEbQder+oYTAWbtAWK6KQXeojodH4mBwcyrAHrwXxYB8QMDrUeAqC6a6PRL68fHBAjnbVttbqXK1lau1rhV/wA9HPgZuuCilotLGp14TBj4lx0XmAXCMlEt/AaCVoTOiPDkaLrIzigxDgXQojdoBOdEG3aAWCbi3SK6KQXdH6aj57CYX29WPuBxKa7W+0P9KgHOtETQ++2QEgkpIhell8WczuX04X++f3DXmMlpPvW5XAFIxP0IBtjLNJeWNQGInprZyuaO8Uq5/Gc6EERvdWaIjrjXPQh2A5SnJEBqLNkrD4rgxBCeqHFwqK8SCd2RysR3SYDShTRSS/o/DQfPQYy5DlJUdS4GBKd5mb1cVGRdvuVi4uyLSQGz0fEDA63iYzzeCI6oEa6WFFED3XfpuoSTYRsFA9ljEWq4qEdIizkdzovJy+ug1uK7FZ1ojd1GhDnUpy9cS6pDiYB9mgLhBASES2d6IFA+AUoIXaBTnSS7ei5sKjF24Nl0KMOPB51kNzig3uWQIq3Xi/gdmu3XzrRk4MiOjEDuVBoWYHNRfSjzk698tAlWSmiaxTnYuUICzmYJGdbxEI60a2aiW5knEt1SzX8Ab9ux7ESwTiXNJzodmgLhBASES3EQ69XXYyRFyfEjsh2kM6iogCd6MS+ULQyHz3qAGA9JINedUAnenLwfETMQIqHUiCPhaVF9KOilJ4RFkB2ZkFrGeeiKIpWxdKUZAaT5KyN2rZaS74fIxYWHVAwAE6HE37Fb8nzgR7QiU4IyWqyLMaCkIjQiU6yHT0z0Zubga4u7fabqehRBwAXeE0GDmRYA4roxAwSzUQHLC6i04muC+1d7fD5fQBSd+BKEd2v+NHa1apV0TQlmVgj6VbvCnQFc9StRDATXce2kOPMCTrys6UthC4smipBJzoz0QkhdiPLxENCIpJl7YAiOumFHqJVaan6uKFBu/1mKnrEuQC2OS9ZAr3qQDrRGecSn44OoLNTPOZCx8QoFEXJmDgXIxZTBLJPRJcudJfDhSJ3aotm5OfmI8eZA8C6Dtxk2kFebh4KcgvCXmcljHCiA9nXFtJdGwBQBzas2g4IISQqWiwsCtCJTuxNls3IoIhOeqGHeOhyqSIYhav40AVtPqwD85F1AACFhdrtlzMySCxaOluCLmPbO9ENyIEGsls4THXBVofDEawXKfBajdq2WgCJtQNAdaPL11kJ6XJmW9AWLeJc6EQnhNiWLHPgEhKRLGsHFNFJLygemg/rwHz0rgM60eMj66CoCHBq2FvJOmhoAPzZsfYbSQIphufn5qPAHd9ZZGkR3WdsJvrBloMIKAFdj2UF0l1UVCJd7M0+68WfAMktLBq6ndVEdEVRjJuVUZhd6wNo0RZknXBhUUKIrQgEgPZ28ThLHLiERERrEb2+XrQvi0IRnYShKBRwrUDz0etp1oF5cGFR89GrDvqEGOYYL0V6kkwONGBxEb3DmEz08oJyOOBAd6DbcgKqHqS7qKjE6k70ZNuCVUX0ju4OdAe6AejvRB9UPAhA9jjRg22BcS6EkGyjo0N9nCUOXEIionWcSyCgCmIWhCI6CaOjA+gW1xkUcE1EnjO0jLAAwgf3SGzoRDcfveogN1e42wGej0hvksmBBlQRvaa1RrcypYpRTvRcV27wc8gG8VCLHGgAKPIcdaJbcCFOIPm2ILeT4rtVkO3AAUdCs0vSgXEuyRN0ojPOhRBiJ6RwCAB5eenti050Yme0EtG9XrUtWfginSI6CUOvDGKAAm4ytLSI+6LU1iuLCrOgE0dvJ3pHhzoDkERGrzoAeD4i0UnVfdvW1YaO7o44WxtLMMJCZyc6kF3ioXTfphvnknFO9DxrOtHl51vkKYLToe+lj2wH2Rbnks6AkmwHVo01IoSQiMhFRb1esQBcOtBtSOyMViI6YIuLdIroJAwp3hYUaJtBDLBvSAa9neisg/joJeAWFam/s1gPsTFCRGcdkJ7IWBbprI5Hsac4KMxJcdUqGOVEB7JLRJexE6We0rT2Y3URXYrhds9ENyoPHQAGFWVPnIuiKJo40a0+I4MQQiKipXBIJzqxM3q0BQtfpFNEJ2FI8VZrBzRgi/ZgGeRgBkV089BLwHU4GOmSKHqej9gWSDSCERYJum+dDmfQkWw5Ef1oJrreOdBAdonoWmXNW3lhUV+3Lyg+JxrnYlUR3Yx2UNtWC1+3T/fjmUlrV2swa14LJ3pbV1twf4QQYnmkcJhulAvACxNib/Rwolu4LVBEJ2FQtLIGetUD6yBx9HRBc3HRxOD5iJhBMMIiQeEQgHVFdJ8xC4sC2eXAlZ9rqbc0rf1Y2YkuhXCXw5Xw+7SqiC4/XyNE9L55feF2uQEA1S3Vuh/PTGSUS44zBwW5qWfNy8EkAGjpbEm7XIQQYggyl1MLEZ1OdGJnsmxWBkV0EoZeDmiAolWidHeri33r6URXFG33nWkYIeDSiR4bPc9HnBlDopFsnAsA9MsTI2N1bdZq1EHHNONcNEXGuaT7uQad6BaMsZCDSf3z+yecI27VhUWNXBvA4XBkTS56aJSLw+FIeT+eHE9w4MGKA0qEEBIRecFOJzrJduhEJ9kMnZ/m0xJiwtHLid7ZyUUt46GngEsnemLwfETMINnFFAHrOtGlOGtkjMW+pn26H8tstHL4W9mJHow1SmJGhlWd6LK+jGgHQPbMytAq1giwdrQRIYRERF5Me73p70u6e1pagK6u9PdHiJFQRCfZDEUr85HibW4u4HZru++CAiAnRzxmPcSGbcF8WAfEDFIRD6WIXtduHSd6d6AbHd3CJSUX7tOTwcWDAWSJiK6Rw9/KInqyi4qGblvfXm+pbOtgnIvbGBE9W2ZlaLlwsZXbAiGERERLJ3ppqfrYwjEWhESEcS4kmzEizsXC7cESyDrQQzgMXdSS4mFsjHCiM84lNkacj1gHpCepONFlnIuVnOihjs7QzGG9GFIyBID4/KR4n6lo5USXgxtWjnNJZUaGAsVSbcHIOBdAdaJn+oCSpk50C7cFQgiJiBTRtXCiu1xAydFzKcUSYjeybJFdiugkDD2dn6EZxIGA9vvPFGQd6CEcArY4L1kCuqDNx4g64O9UEkp7V3tQAO6X3y/h11kxzkWKUR6XB7muXN2P18fbB3k54sfz/qbMzoLOBid6cEZGEiJ6jjMn+J7kopNWQNaXUXEuclbG3qa9hhzPLLR0osuBPiu2BUIIiYiWC4sCvEAk9kW2BS3jXCx8kU4RnYRhhIgeCKjHIb3R04kOsH9OhM5OcQPogjYT5tITo5EiuMvhSsq9bcU4F+lENyLKBRALKko3eiaLh4qiaOdEt3AOdNCJnkSsEaC2BbnopBVo6jwa52KQiB5sB42Z2w4AbZ3osm6s2BYIISQiWjrRAVvEWBASES3bQqjz1qJQRCdh6OmCzstTB2ot3CZMh0508wld3JUCrnkY4UTnQAYJRYroffP6wuFwJPw66Vq3ohPdiCgXyZDizBcP27vbg3nfmexElwNCMqooUfp4xcWPlZzowUx0o0T04swfTAI0dqIzzoUQYjfoRCdEoGVbsEE7oIhOwqAL2nyMqgMOckdH1oHbrf3irgAF3ETRc0Ap9FzEeCkiCRXRk8GScS4GO9EBZIUTXbpvnQ4nCt3pnZysLKJLETzZttAnT4joVmoLWsXvJIpsB/ub9sMf8BtyTDOQn2uptzTtfclFX63YFgghJCJ0ohMCKIo+TnQLtwOK6CQMPZ2fAEX0RNDbiW6DGTKmo3c7oBM9MfQcUGK8FIlEuiJ6XZt1RsakozNdoTcZBheJLOhMXlBRum+LPcVJzVaIhBzgCHW3W4VU20LQiW6lOBeDneiVhZVwOVzwK35Ut1Qbckwz0MWJzjgXQohd0NqJbgPxkJBedHYKIR3Q1one2gr4fOnvTwcoopMwjBLR2TdER88caIADGYlgZB3IPof0Rs/zEeOlSCRSFQ5l5IWV3LdBJ7qRcS5Z5ETXcjFFwHriYboDStkc5+JyujCwaCCAzG4LDR0NALTNRKcTnRBiG7R2ovMindgR2Q4AbdpCSQkgTSoWFQ0popMwjBIPGWMRHc4GMB+jnOg+H9DWps8x7E4gIAagAf3OR7IeeD4iknSFw/budrR3tWterlQIZqIbGeeSBZnoUjjUIsLCk+OBx+UBYL0saOkkl/EsiWJlJ7oWYm+iZMPiopo60d3MRCeE2Aw60QlR24HDoU0OrtMJlJaKxxZtCxTRSRgUcM2HTnTz0bsOCgqA3FzxmPUQGSmgAzwfEeNIVUQv9hTD5XCF7cNszHSiZ0Oci1aCrBzksJIDt8vfFSyP3TPRFUUJvhdTFtnNYCd6cFYGneiEkGyETnRCwttBmjGHQSzeFiiikzCMEtHp/IwOBzLMR+86cDjYFuIh68Dp1O63aU/YFkhPUhXRHQ6H5RYXDTrRDRQOBxeLTPS69jq0dWXmNButF6m0ongo3fZA8o57qznR27vb4VfE4p5GxbkA2TErQ5dMdDrRCSF2gU50QrRvB4DlM6ApopMw9HbgckHF+BjlRLfoOckS6F0HANtCPEIXFdVqULsnFNFJT450pCaih77GKiJ6S6doREbGuZR4SoILmWaqG11zJ7rbegsqSgG82FOMHGdOUq+1WiZ66Oda4C4w7LhZtT4AneiEkGyETnRCtG8HgDqgZNG2QBGdBFEUOtGtQKh4qAcWPydZAr3bAcC2EA8j6oCZ6KQnqTrRAaBfvvhC1bVb4wtlhhPd4XBkvAM3G5zo6bQDGediFSe6bAeF7kI4HcZd9mR6nIs/4A9+tppmoltoMIkQQmJCJzoh+jrRLSpYUUQnQXw+oLtbPNZ7QUWLtgdLIMVDvZ3ozc1AV5c+x7A7dKKbjxF1YPH+2XQee+wxVFVVwev1YsqUKVi7dm3UbRcvXowzzjgDffr0QZ8+fTBt2rRe21999dVwOBxhtxkzZuj9NpIiHfHQak70YCa6gU50QI10yXgnusYiupViLNIS0b3WykQ3Y20AIPMXFg39vmrhRGecCyHEdujpRFcUbfZJiN7o6US36IASRXQSpDnkd2uBTjNe6fyMj95OdLnYMWDZ85LpGOlEp4AbGdaBuTz//POYN28eFixYgA0bNmDixImYPn06Dh06FHH7VatW4YorrsBbb72F1atXY8iQITj33HOxf//+sO1mzJiBgwcPBm9/+9vfjHg7CZNRIroJTnQg8x242bCwqIxikYJ4MgSd6FaJc+k0ZzBJtoPqlmp0+jsNPbYRyBkZbpcb3pz0L5ytOCODEEJiopcTvbNT3TchVkd+V7UU0S1+ka67iJ6NTja7IsXbvDwgJ7kIzIRhhEV89Haiu1yqkM56iIyRTnTWQWT0bgeA5ftnU3nkkUdw3XXXYfbs2Rg3bhwWLVqE/Px8LFmyJOL2zz33HG644QZMmjQJY8aMwZ///GcEAgGsXLkybDuPx4OKiorgrU+f5EU6PUkrziXvaJxLmzUatVlO9Ex34Goe5+K2nnioxWBSe3c7Oro7NC1XKsh2ILP6jaKsoAxulxsKFBxsPmjosY1A6xkZcrCvrasN/oBfk30SQoiuaO3ALSxURRhenBC7INsB41y0IVudbHbFyAziI0eAQEC/49gZxliYD13Q5qP3jAyAAxnR6OzsxPr16zFt2rTgc06nE9OmTcPq1asT2kdbWxu6urrQt2+4CLdq1SoMGDAAo0ePxpw5c1BnoQ+/098ZXIwzFfGw1FsKQBWXzIZOdH3Qy4lupSzodET0Yk8xHBCrQVvBjR5cYNfgduB0OIPRRpnYFrRcVBRQnegAI10IITZBaye6w2H5GAtCesE4F23JViebXTFSOAwEgCbrmK4sBRdUNB8jBzJYB5GhE908amtr4ff7UV5eHvZ8eXk5qqurE9rHbbfdhoEDB4YJ8TNmzMAzzzyDlStX4sEHH8Tbb7+N8847D35/ZNehz+dDU1NT2E1PpODngCMld6UU0Rs6GjQsVeqY5cDN9Ex0Wb+ZvLCoXBQ0lTgXp8MZbAtWWFzUrDgXABm9yK7WTnRPjge5zlwA1hpQIoSQqOghHvLihNgNLiyqHdnqZLMzRgiHXi+Qny8eW7RNmIqicFFLK2D0rAzSGyOc6Bbvn23LL3/5SyxbtgwvvfQSvCEXFpdffjkuvPBCjB8/HjNnzsS///1vfPTRR1i1alXE/SxcuBAlJSXB25AhQ3Qtt3TflnpL4XK6kn695UR0s7KgSzLcia6TA9dK7tt0nOiAtXLRzVpYFMjstqB1OwCsOaBECCFR0SML2uIOXEJ6oedgkkXbgW4iulWcbIDxbja7YoRwCNCBG4uODkB+lelENw860c3H6EgdRdHvOHajf//+cLlcqKmpCXu+pqYGFRUVMV/761//Gr/85S/xxhtvYMKECTG3HTFiBPr3749t27ZF/P/8+fPR2NgYvO3dq68Qla5waDkR3STxULpvGzoaglEamYR04Mr6ThdZP1YSDtMW0Y862C3lRDdDRM8CJ7pW7QAIiTay0IASIYREJQuzoAnphR5OdDmYZNF2oPvCoqmilZMNMN7NZleMEtHpwI1OS4jeUFCg33Eo4MaGTnTzMTLOxe9nvFQobrcbkydPDotSk9FqU6dOjfq6hx56CPfddx+WL1+Ok046Ke5x9u3bh7q6OlRWVkb8v8fjQXFxcdhNTzJJRPd1+9AV6AJgvBO9yFMUjHjY07jH0GPrjaIo2i8sakH3rRS/U20L8nWyTZmJWQvsApm9PoDW7QCwZlsghJCIKAqd6IQA+jvRLeh0001Et4qTDTDezWZXjHDfAhRwYyHroKAAcOo4xEUnemyMXtzVgn2D6RgR55KXpw6aczAjnHnz5mHx4sV4+umnsWXLFsyZMwetra2YPXs2AGDWrFmYP39+cPsHH3wQP//5z7FkyRJUVVWhuroa1dXVaDlakS0tLbjlllvw4YcfYteuXVi5ciUuuugiHHPMMZg+fbop77En6YroUkyygoge6uQ0OhMdAKpKqwAAuxt2G35sPWnraoNfEdPFNF9Y1ELuW9kWZCxLslgqzsVMJ/rROJdMG0wCtF8bAFDriJnohBDL09mpPqYTnWQzejrR/X7VWWchdJPprOJkA4x3s9kVOtHNxwj3LcA6iIeRUSJdXeEzEIjAqLbA36qRueyyy/DrX/8ad911FyZNmoRNmzZh+fLlwYi2PXv24ODBg8HtH3/8cXR2duLiiy9GZWVl8PbrX/8aAOByufDJJ5/gwgsvxKhRo3DNNddg8uTJePfdd+HxeEx5jz3JJCe6FKHycvKQ48wx/PjD+wwHAOxs2Gn4sfVERli4HC4U5GozXcyK7ttMjHMxYzBpWMkwAMDuxswaTAJCFhbVMBPdigNKhBASESkcAnSik+xGDyd6Xp66PwtepOt6ZTVv3jx873vfw0knnYRTTjkFjz76aC8n26BBg7Bw4UIAwsl211134a9//WvQyQYAhYWFKCwsREtLC+655x7893//NyoqKrB9+3bceuutlnKy2RmjRXS6oHtjhPsWYB3EIhAAWlvFYz0F3Px8wOMBfD7RN+hd53bDqLbQty+wf78l+2fTmTt3LubOnRvxfz0j1Hbt2hVzX3l5eXj99dc1Kpk+BN233tTct1JE9/l96OjugDdHwx+TSSKzyM2IsACAqpIqAMCuhl2mHF8vZIRFsacYDodDk30GFxa1iPtWUZSggzxtEd0CTnQz24KckXGk/QiafE3Bus4EgiI641wIIdmIFA4dDsDt1m6/dPcQu6GHEx0QbeHAAdEWqqq03Xea6JqJno1ONjvDOBfzMdp9yzroTVubGq+ip4DrcLAeYsFBPWI0UhRKNcKiyFMEB4SwarYb3cwIC0AVDzNORNfDfWuxhUVbu1qDefqpDigFM9E7zBcBzFpgFxDnhH55opPJuLbQoV9bsMqAUqby2GOPoaqqCl6vF1OmTMHatWtjbt/Q0IAbb7wRlZWV8Hg8GDVqFF577TWDSkuIRQl132o0qA6ATnRiP/RwogOWbgu6z/HNNiebnWGci/kYNZBB4TA6sg4cDuEW15N+/YCDB9kWIsE4F2I06Wb8Oh1OlHhL0NDRgMaORlQUxl7/RU/MXEwRyGARXcfFFH1+Hzr9nXC7NHS0pYCckeF2uZGfm1onKIVV+XmZSXBAyaS2MLzPcNS112FXwy5MKI+9zpOdoBPdnjz//POYN28eFi1ahClTpuDRRx/F9OnT8cUXX2DAgAG9tu/s7MQ3vvENDBgwAP/4xz8waNAg7N69G6WlpcYXnhAroaf7FuCFCbEPeiywC1haRNfViU7shVEiOt230eFAhvmEirdaGgsiwbYQHSPjXAC2BaKKQjKWJRWskotOJ7o+6JkDDVjDgSsjWPp4+6QcWSOFVfl5mYmZTnRAbQs76zNsfQA9nejMRNeNRx55BNdddx1mz56NcePGYdGiRcjPz8eSJUsibr9kyRIcOXIEL7/8Mr72ta+hqqoKZ555JiZOnGhwyQmxGFnoviUkIrItaD2gJAdrGxq03a8GUEQnQSjgmo/RTvT29vB1UYhxdQCwLcSCTnRiNEEnehqikGVEdIs40Q+3HQ5mUmcC6c5WiESOMyeYn28F8VC+x3QGk+hEVxlemtmL7NKJbh86Ozuxfv16TJs2Lfic0+nEtGnTsHr16oiveeWVVzB16lTceOONKC8vx/HHH48HHngAfr/fqGITYk3oRCdEkIUDShTRSRBmopuPUQMZRUVAztEwJ9ZDOEbVAcDfSdFQFC6yS4xHCn5piYdHBSXTRfSjwmGh24DRwAiUeEuCedq7G3abUgY90MN9C1grC1oTEZ1O9CAZOytDDye6h050PamtrYXf7w+uTSYpLy9HdXV1xNfs2LED//jHP+D3+/Haa6/h5z//OR5++GH84he/iHocn8+HpqamsBshGYfewmFDAxAIaLtvQvRArwEliujEDtCJbj5GDWRwUcvoGOlEZx1EpqMDkCYnxrkQo9DCZWw5J7pJwiGQmeKhHu5bwFrioZaxRmY70RVFCc6EMGtAKROd6P6AP/hdpRM9swkEAhgwYACeeOIJTJ48GZdddhnuuOMOLFq0KOprFi5ciJKSkuBtyJAhBpaYEIPQWzhUFKDR/IFoQuJCJzrJZozORG9oUIUyIjDKfQtwMCMaRjrRWQeRaQ7RkQoK9D0WRXQiYSa6tmSkiK7BbIVIWNGJno7DWL62ydcERVG0KFZKtHW1QYE4vpkLiwKiHZj5WWhJ6GCPLpnoFmgHmUj//v3hcrlQU1MT9nxNTQ0qKiIvhF1ZWYlRo0bB5XIFnxs7diyqq6vR2dkZ8TXz589HY2Nj8LZ3717t3gQhVkEv4dDjAfKPLuptQfGQkF7oNaDETHRiB4yOc1EUS7YJUzEqBxpgjEU06EQ3H1kHBQWAU+deiiI6AYCAEtAknsAyIrrJmehAhoro2eBElwMFntKU9yE/H7/iR2tXqxbFSgn5eTrgQEGuziOyURhWMgyAGFCo78gMQUR+R9wudzDPXwus1A4yEbfbjcmTJ2PlypXB5wKBAFauXImpU6dGfM3XvvY1bNu2DYGQWIkvv/wSlZWVcLvdEV/j8XhQXFwcdiMk49BLOAR4cULsBZ3oJJsxyoGbmwvI31MUD8Mx0olOATcydKKbDweTiNG0dLYEHat0omuDFNEzKcYiKKJnQSZ6Ou8xPzcfLocrbH9mID/PQnchHA6HKWXIy81DRaFw+WbKgJJug0kWageZyrx587B48WI8/fTT2LJlC+bMmYPW1lbMnj0bADBr1izMnz8/uP2cOXNw5MgR3HTTTfjyyy/x6quv4oEHHsCNN95o1lsgxBroJRwClhYPCekFM9GJ1uzaBZx9NlBVBSxZYnZpotPZKW5A5gm4HR3ANdcAAwcCc+cC3d36HzNVMlk8/O1vgWHDgOnTgYMHjTlmKmTyQMY77wDHHw9MmAC8954xx0wFM+rgyBExO4ZkJ1LoS9dZGRTRfQ3pFyoNgiK6iU50mQWdKcIhELKYYiY70TWINXI4HEER3sxcdCu0AyBkQKk+MwaUdIs1slA7yFQuu+wy/PrXv8Zdd92FSZMmYdOmTVi+fHlwsdE9e/bgYMiP9CFDhuD111/HRx99hAkTJuDHP/4xbrrpJtx+++1mvQVCrAGd6IQI9BpQsnCcS47ZBchkAgHgO98BNm4Uf19zDXDsscAZZ5hbrkhI0QowTsDdtcuYvuGuu9QBjMceE2L6z36m/3FTwYxMdCME3FdeAW6+WTzeswe48krg//5PLHBqNcwYyDCiHdTUiPORrO8LLwS++AIoK9P/2MliZB3I36l+vzguZx1nJ1qJQlZZUFEupmgFJ3pGiejZ5ERPc6CgxFOCI+1Hgp+ZGVhhgV1ADCh9uO/DjJmVoVc7kIu/tnS2IKAE4HTQ66UHc+fOxdy5cyP+b9WqVb2emzp1Kj788EOdS0WIzaATnRABnehES5YtEwJ6cbFw3wLA/febW6ZoSNHK4xFxK3pjlAN31y7g0UfF429/W9z/+tfhCxdaiUwUcBUF+PnPxeOZM8V3bNUq4IMP9D1uqmSqC/oXvxDtbcwYcauvBx5/XN9jpoqRkTp5eervXxo+shcthcPQ/ZmFlTLR69rrLCEOa4FuTnS3dRy48rub7oASnegqmTagpNX5siehgx2tneZl6RNCSFyMcKJbUDwkpBdGZKJbbLo4RXQd+eMfxf1Pfyoc0ADwxhvATgsaUYwUrQDjBNylS4GuLuCss4AXXhAzAerrgeef1/e4qZKJi1q+9RbwySfiPT35JPDd74rn//AHfY+bKma5oJua9DtOayvwzDPi8e9+J2ZnAMCiReLYVsPIgQyAuehEmwiL0NebLqJbIBO9yFOE/vn9AQDb67ebVg4t0c2J7rGOE12rtiAFVjOd6HJGhnQ4m4WMNsoYJ7oGizBHIj83P+g+t8KAEiGERMUIJzrdPcTqdHWpYoJecS7d3UBbm7b7ThOK6Dqxaxfw/vsiruL73wdGjgTOOUcMovzzn2aXrjdGi+hGCLiKogqH114LuFyqgPuvf+l33HQwY1FLvYXDv/9d3F9+uaj3a68Vf7/2mjjvWg0jBdy8PNXAoOfvpH/9S4j08jz03/8tfp8dPAisXavfcVPFyIEMgNGDRJvFFAELiegWcKIDwLF9jwUAfFX3lanl0AJFUbLKiZ5uW7CEE90icS4j+owAAOyo32FqObRCr4VFHQ5HcMDDCgNKhBASFTrRCVEHkwDt20JBAZBzNH3cYm2BIrpOvPSSuD/zTGDQIPH4W98S92++aU6ZYmGkAxowxom+ebNw/eflqVEuF10k7lessNyAFgBj68EIEd3vV9vCJZeI+5NPFjncTU3WXNzSLAFXz3r497/F/SWXAE4n4HYD3/iGeG75cv2OmypGO9EpohOtM9FNF9GPirFmO3CP6XsMAGDbkW2mlkMLWrta4VeE20Y3J7oFRHTN1wcwMxPdInEush3sqN8Bf8CC07+SRK/BJEAd8JCzCAghxJLQiU5IuIju8Wi7b4fDsrnoFNF1QgrlUjgHgHPPFffvvKMOXlqFTHSiv/66uD/rLCA/XzweP14ManR0WM+B6/erwr6RTnQ9++dNm4BDh8S6AGefLZ5zOoEZM8TjFSv0O3aqmBUlolc9dHerQnno+UjWgWwnViITBzKItdEq41cKh+3d7fB1+9IsVWooimIZB650omeCiC6FQ5fDhYLcAk33bZWFRRVF0Xx9ADrRgSElQ+BxedDp78Sexj2mlkUL9Io1Aqw1oEQIIVGRYg4XFiXZjGwHHo8QebRGRro0NGi/7zSgiK4DPh/w9tvisXR7AsDYsUBFhRBw1683p2zRMCsTXU/R6o03xL1c1BUQA1pf+5p4bLWFLVtD1lAyMo9bz0Ut33pL3H/96+EL1p5xhrj/8EN9jpsOmRYl8skn4jdYSQlw6qnq82edJe43bAgfRLYCRp+P+ovYZtTWGnM8Yj20yoEu9hT32qfRdHR3BB3TVnHgfnXE/nEusj6LPcVwOBya7tsqwmF7dzu6AiJnLRMy0a2wNgAAOB1OjOw7EkBmtQU9nehmDygRQkhM5MWTHnEuFhUOCemFnjMyAMsOKFFE14F164SjuKxMOJ8lDgdwyinqNlYi0+Jc/H5g9WrxWDqgJaedJu6tJqLLOsjJ0X42TCRkHfj9QKNO17hSRO9ZB1LMXbvWegtbmhUloteAkvyeT50q1gWQVFWJc1RXl5gxYCWMroOyMnFPET170cp963K6ghEqTT4dVwuOQagQa3acy7H9Ms+Jnq64HAmrCIfyPTodzrS/O8FMdDNFdIusDQBk1voAei0sClhnQIkQQmJCJzoh+q4NAFi2LVBE1wEZEzJ1qhDOQzn5ZHH/0UfGlikemRbn8vnnQogrLASOOy78f1Onivs1a/RzYKdCqANaY5NbRLxeNeZGj3pQFFXAPfPM8P+NGyfeZ0sL8Nln2h87HYx2ous9oCTrQM7AkDgcwJQp4rHVoo2MrgPpRD982JjjEeuhlRMdMD/GQgqHBbkFcDrM/ZknnegHWw7aPuM4GyIsQgeT0nXbm90OAKClS3znzHaiAyEiOp3oMbHKgBIhhMSETnRC9HeiW7QtUETXgTVrxL0UqEKhiC7QWziUMSGnnBLuvgXE7ACnU7hOq6v1OX4qGO2+BfSth+3bxfnO4wEmTAj/n8sFnHiieJztLmijnOhyBkYo8nxk1ZkxdKITowiKhxoIpDLSxWwnuhXct6XeUvTPF6NU249sN7k06WHEYopmC4eaDiZZyIlu9owMIMOijehEJ4RkO0Y40RsbrTdlm5BQ6EQnWiFFdBndEsoJJ4j7bdvURSStgNFxLlI4bG4GOju137+sg9AMaEleHjBqlHj8ySfaHztVjHbfAvoKuHKgaNKk8Dx0iYw62rxZ+2OnSmen+n3MBCf6/v3A7t1i0CjS+UgOblipDgA60YnxSBFdC/HQdBHdIospSjJFPDTKia6YOEVOy8EkKzjRrTSgJKONMiLOhU50Qki2Y4QTHQCazPktSUhCMBOdaMGhQ8CuXSIqQbo8QxkwQAg2igJs3Wp48aJitBO9tFSNLNGjTUgneiQRHVDFQyuJ6EYPZAD6LvAqRfSTTor8fymif/qp9sdOlZaQtAGjB5T0rIPx4yO/n+OPF/dbtljL6GD0+YhOdKKly9h0Ed1CwiGgxljYPRfdCCd6QAmgvbtd8/0nipa571ZyolthQEm2g50NO9Ed6Da5NOmhqxPdTSc6MYhDh4ClS4EvvzT+2B98ADz8MLBvn7HHra8HrrgCmD5dZJ8ayf/9n1iQ6eSTjX/fgPYLgOkpHrrdauaqETEWb70FPP880G1w39TeDjz2GPCPfxifsfviiyLj9+67jT12Vxcwa5YQYX75S+OOK2lrAwIB7fZHJzrRAilajRkDlET5bTlunLi3Uha00aKVy6W2Ca3dn21t6u+CSO5bwJoiutF1ABgjokcaTAJUAddKIrqsA48nsnteD/R0osuoHDkDpifDh4vffh0dwI4d2h8/VYweUAp1oltpnQRiHFo60c0WD2X2uBWEQyDEiW5zB65Wi89GosBdEHxspgNXy/co9yH3aQZWGlAaVDwI3hwvugPd2NWwy+zipIw/4A9+rroMKHnoRCcG0NoqFguaPRuYPFlkUBrFunXA178O/PSn4r611bhj//SnwLJlwBtvABdfrK2YFouuLuCqq8T02HXrgLlzjTkuIES+r39dOPhuvlnb/QL6iYfSja63eLh0KfBf/wVcfjlw4436Hqsns2aJ78IllwB/+INxx92xQ7zfDz8E7rkHePZZ4469eDHwl7+IC//5841dmOxf/xLuvYoK7QQYZqITLZCirMx7joRc6NLoAeBYmOGClu5PrUX0zz4TQtiAAUB5eeRtxo4V92aYD6KRSU50vx/YsEE8jiei79+vvTkgVczIpZdOdD1E9I8/FvcTJ0b+v8ulDupZKdLF6AElKaJ3dobPRiDZg5ZRHcVui8S5WEA4BFQRfVu9zZ3oOsa5OB1OFOQKId1MB66Wg0lyH02+JtMiaqzkRHc6nBkxoBT6/dSjLcj8ejrRia4sWyayVQHxw2/hQuOO/fDD6vTPnTuBp5825riNjcDf/qb+vWUL8Oabxhz7tdeAgwfVv195BThwwJhj//GPwLvvise//a12oqVRMRZ6iofd3cAdd6h/L16stgu9+fxz4UCX3HuvPhm/kXjqKTGwI3n0UWOOCwBLloT//fjjxhzX7xeDJD6fEN/mzdNmv3SiEy2QgzpSIIyEFK2sJKKb4YIeMEDcay2iSzFQxoVE4lgxq9awfiIRzKgDvQYydu4UMwI8HmD06MjblJSo3wEjDSCxyLRc+ngiOiBmzQDWHFAyqi0UFKh9PyNdsg9ftw8d3eJiKCMy0TutIxwCaoyFnYVDQN8caMAaDlwt36MUWANKIDg7wmis5EQHQtqCjdcHkFEuHpcH3hztxSPGuRBDeOYZcT99urh/8UVjcg0bGoCXXhKPr7xSPbYR/POfQvAaOxa4/nrx3KuvGnPs554T9z/9qZgmrijCDW8ETz0V/res+3TJBCf622+LwYx+/YCzzxb1smyZfscLRQroM2YIZ3RtrYj8MYJ//lPcP/qocJRt2ADs2aP/cXftAtavF4uV/f3v4rlXXzVmGvRbbwnnomTFCm1ilZiJTrRACrixRHQp4FpFOATMceDqJeAmMpAxcqS4P3JEHwdwKmTSbADZDsaOFX1TNI4RpizLDGaY0Q7kbID6em1nVTY1icEMILaILtuCVc5HXV1ikBwwti1wcdHsJTR2RQvh2XQR/agIKx2dZiPdtwdbDto6okHPHGjAGuKhlk70vJw85DhzAJgTbRRQAmjtFDEJHFDSDj1nZADWGEwiGU59PfDOO+Lx448LV099vZpDqScrV4ofuWPGAAsWiOfefdeYaZBvvSXuL75YCJcAsHy5/sdVFCHWAsDMmerAhRHHPnBATFF3OtXIjtde02bfmRBjIUXrb34TuPTS8Of0Rg4mXXopcP75xh17+3YxCyM3F7j6ahHnBKjfUT35z3/E/RlnABdeKAZgDh8W5dEbKdr/4Adq3rEW71kOJtm5HaQARXQN6epSFwtNRMDdscM6+btmOHD1FtFjOdHz84FBg8TjryxyLZNJAxky7z9WOwBUEd0qAq6ZTvRAQNtYGxktNXiweoxIWG0goznkutmM8xGd6NmHFA6LPcVwOWOM+iWI2ZnoVnOi98nrg/ICka22tdZCK6onSVY50TUQSB0OR/CzkgMQRtLW1QYF4ke2VQaUju0nRPQvj1ho6leS6Lk2AGCNwSSS4Xz4obg/9lixONA3viH+NsIZ/cEH4v7ss8XxR4wQERZGCHjy2KefDpxzjnA5ffWV/ot87tghFnF1u4Vgee654vlVq/Q9LqDGuEycCHzrW+Lxzp3pX/gqSmbEWMjv3VlniRsArF6tDhDoRVOTOl36vPPUYxvxnVizRtxPniwG0M48U/xtRBuUUUJf/7qYrn/aaeJvI963bP/nnSdEfAB477309yu/K3ZuBylAEV1DvvpKCOmFhcCwYdG3GzpUDIi2twPV1caVLxaZ5IJOREQHrCseZkIdSBFd5v9Hw2p1YMZAhscj4kQAbWdFSBE9lgsdsJ4TXdaB2y1uRkEnevYSdBhrJApZxYlulQgLABhXJnLsttQa4LbRCfk90cKlHQkpHpoVfQJo60QHzB1Qku3A6XAiPzff8ONHYkx/kZ9m68EkvWdkeMxvByTDWb1a3E+dKu6liGbEAn9SyDrtNMDhEII2IBbb1JOaGiFmOxzAlClAcbGaL7txo77Hlu958mThVj3xRCGE1NToL4RIEf2MM4RgKjNG0/28u7vV6cN2deC2t6vf+TPPFJ9NebkQRfWelbF+vRiIGDpURLlIEX39ev1nZcj3Jhdtk+3//ff1PW6kY3/96+JeDuzpRVOTmiM9ZYp63pHtIx2MinNpazMuMz8BKKJrSGiMiMMRfTu3W5wzAOsJV2Zkoh86pN0+Dx8WfbLDEV/AlbE6dKKbJ6JLAdcqIroZAxmAPrnoX3wh7uVv5GjIOti7V41RMRMz1gYA6ETPZrQWDk0X0S3mRAeAsf3Fat6fH7bQYjBJYliMRYbEuQAw1YkuP8dCdyEcsX6UG4gcTNrTuMe2cSW6z8hwmz8jg2Q4UrCSIvoJJ4j7TZv0PW5np8hejnRsvYVsKRqPGyfEZACYNEnc6/2+5XueMkXc5+cDo0aJx9KNrBdStJSOXxljke6AiXShA/Z14G7ZItyf/fqJGREOh/r5GFUvUkwePFiI6YGAKiLohWwL8tiyDX71lb4O/JYWNbZFHls63WQOrl6sWycGLYYNE5/zqaeK5z//PPy7nAp6z8goLlYfWyjShSK6hiSShy4JjXQxG79fDO4A9ndByzoYMUJ190ZDuqCtIqKb6USvq9Muj7u7O7FYI4BOdInMRdfSiS4XCpW/U6NRXi7aiqKI9U7Mxqw6oBM9e5GiUMaJ6BZ0ottaRNd4xkJPrCAeav0ereBEt9JgUt+8vqgorABg31kZRjnRGedCdEO63k48UdxPmCDu9+/X90fgV18JIb2oSFyoAsYJ+NKFGjpN2ygRXR471FllxLEVpff7lvnX6YrEoWKrx5PevqKhtxNdiibjx6vuT/k5yTaiF+vXi/uTTlKfM+LYiqJ+5+R3obJSuNn8flXA0IPNm4XYUlkphGxAFUo+/1wIKHohB+mkeF9eLgZpFCV9IUxvJ7rLpQ78WSjShSK6hiQjosu+0wpO9NZW9bHdRfREFhWV0ImuCoeBgHYC7rZt4jdifn7sWCNAFdEPHAj/HpqF2U50M0R0h8Na5yOz6oBO9OwlmPGrkShkpvsWsKZ4aPc4F0VRgoMiXFg0cazgRLfSYBJg/wElo5zobV1t8Af8uhyDZDH19WqEyBgRr4SiIvWCRE9RV7pQx41TRUspJu/era9AJI89dqz6nNEieujUWOnA1dPxvHevuLjOyVEv+mWcS7oX/6GLKeo100lvJ3qk/FspoOjtjJbfCTmAFVoOPUX0/ft7fyccDvV963lsOUU8tA0OHy4EE59P34twOTgg26DDoZ7/0l3UVG8nOmDJXHSK6BpiVye6FG+dTv0GkSKhh4guzwPxYkQAVTjcvVu746eDGeJhbq460K1VPchZWOPGie9ULPr2Vc+LVmoLRrugtY5z8flUV3k8ER2wVi66WXEudKJnL1pnXdOJ3puxZeKiYUf9DrR3pTl11ARau1rhV4Sgx4VFE8dMJ7rM1LbSYBIAjOtvcxFd7xkZIect5qITzZEXioMGhccESAFPTyeqFA5DRbSSEhFlAagim57HDhWypYi2a5d+WcNNTerCpaHvWx5bz6nI8oJ01ChxwQuowum2belNwdbbfQsY50QPFa5kO9i8WbiU9cDvVwcx5KBGaDmMELJHjFC/E4AxAr48duh7djpV0UrPgYtIx5ZtMN1zXia0hRSgiK4RnZ2qCBjaR0TDSs7PUOHQyNhImYleWyvOp1og3beh54hoDBki7g8d0n8R6kQwY3FXQB3M0CqbXv5mSWQwCVDd6nv2aHP8dDDLBa11nMuOHeK3YVGRmLEVDyuJ6Ga3AzrRs4+gE12HhUUVvS5CYmBFJ3p5QTn6ePsgoATwZd2XZhcnaeR3xOVw6bZIpdlO9O5Ad1C01GpAqdQj9mOKE92CC+wCGeRE12lGhsflQY4zBwAjXYgORHJkA8ZMT44kZBtx7NBYk9D3XVkpXLCBgH55jlKgq6xUXVNAeKaqXr+TIsXIDBsmXMgdHaq4nwqZ4L4NnRkhGTVKxGc0NYlp4nqwZ49we+Xmhk9Zl/Wk52CS3HdPh5kRgzqRhOzQv/U8tmyH8n0C6rkgXRE9E9pCClBE1wgpWhUUiH4iHlJE37lT33IlglmilRQOFUU78VD+/pC/R2LRt6/47QCk149qhdkLKmrlwJXn4kQGkwB1MGPvXm2Onw6Z4kQPjXJJZGBMLnScze1AOtEpomcfWmeiS3GpK9CFjm7jR2it6ER3OBxBN7odI11Cc6D1WqTS7Czo0JkTGZGJHrKwqJXIGBFdJye6w+GwxPoAJEOJdpFixCJNkeIcjDj2oUPix7XDoR4LCP9br2NLd07PC3MphDQ0aJtlGenYoYJpTo7qHEpn0MLu7tvOTvXCO7Ru3G71olCvKeKyHRxzjKgPiayXAwfSX+wyGtHclka4W6OJ6Hof+8gRVeQJbQuhszLSwYi2QBE9c5Hn4URFKykc1tToN4MqUcxy3+bmqm1CCwG3rU3tDxKJsHA41H7CCi5osx24WonooW0hEWQdWEFEN6staC3gJpqHLrHiQIbd2wGxD1o70UNFOzMiXRhjoT16C4eA+QuLynaQn5uPXFdu7I0TxMxoIyvOyABUEX1Xwy60dlpgMZgk0XthUcD8ASWSwUj3mhTsJEY40aXbe/hwY48tL3IHDuy9CGaoI1wPZGZqz0Wy8vPVGBu9BPxox9bi8zbCfStFdD2Ew927hfszP1+NBpDonTkcTcju21eNWNLLZRrtAjn0PesxMyIQUL/nPY8tRXS9Pm/5PR80KPziWqsoACPbAuNcMg/ZJhNxQANCNHO7RTs9eFC/ciWCWaIVoK1wJQfw+vRRXe7xkOKhlUR0OzvRFSX5tsA6UH+/aBWpY2cR3Wwnen29vgukE+uhtRPd6XAGhTujxUNFUVQR3UJOdMDeDtxsEA61XlQUMFlE77SmiF5WUIb++f2hQMHWWh3zl3UiGwaUSAYTTViVYvKuXUBXl/bHbWpSBaBox9ZbyJaupVC0cqPGO3bP9wzo/77loEVVVfjzsizpXPQY6b71+bTPnZWC7YgRvd2fejuj5bFDZ0UAohx654tGG8gaNkwcv6VFnynJhw6JenQ61Ytuid6DFrIN9mwH8nxw6FB6zn860Uk6JCtaOZ3qAKzZwpVZwiGgrYCbbB0A1nFB+3zqbzY7O3APHxa/E0P7wXhYUcA1ug5kbnlNjTb7S1VEr642f2aMWYN6ffuqvyO1itUh9iDoRNdQIDVLPGzrakNAEYtlWS3GwtZxLlkgHOqxYKRsB2YMDFg1Ex0AjisT2a8cUIqMPHfRiU40Rzp2eoq6AwcKEai7Wx9XjxSy+vbt/QNXiol6CYexhGyjBLxIx5ZirR557IoS/dhaCDBGuG9DF6vTWjwMFdF7orczOpHvhB7HjvWd8HqFUxvQpx3K4w4aFL6gKaC+5z179BnAiyai9+0rcqgBbdYHoIhOUiEVAVeew83OITbTia6lAzeZPHSJVeJcZB0A9hbRZR0MGZL4udRKIrrZTnSzRPSyMuvNjDG6DlwuNZueuejZhRSFtHTgmpUFLUUnBxwoyC0w9NjxkE70L+u+hK/bZ3JpkoNO9NQwa0YGYF0nOmDzWRkaz9yJRLAt0IlOtKS9Xb3g7CmiOZ36XhTGEg7lcevrgVYdIp5iHVvvizCzjl1Xp36WPR348rjpCDBGuG+dTv1iLGKJ6HoPrEQbyAo9th5C9pEjIvsXiDwrQ08BP1Y7qKgQ3yO/X5+2IAepeh47NNdYli8VZFswYmFRxrlkHsnmQAPWEdHNct8C5jvRrSLgyjrIyxNinpFYpQ727RORYWZithO9tlb0oenQ1CQc5UDiA0pWnBlj9/NRJvDYY4+hqqoKXq8XU6ZMwdq1a2Nu/8ILL2DMmDHwer0YP348XnvttbD/K4qCu+66C5WVlcjLy8O0adPwlZ75owmidSY6YJ4TXYpOhe5C3RbATJUhxUNQ6i1Fd6Dbdm50Q4RDs53oPu0HCsyMc7FqrBGgiuibD282uSTJo8eMhZ4E2wKd6ERLpHhXUKCKMqHoeVEYS0QrKVGzoI0+tp5TsmM5fwF98zzlcaVAGYqWTnQ9RXRAPxE9WtRN6HPpCKuxMOv7KI9bXh653uSx9RDmognZgLgIly54PY4d6/PWIhfdyEV26UTPLFpagP37xeNkXNBWEXAzRbTKBCe63SN1UqmDQYPEYGhnp/nipZl53A6H+L2Zrgta1kF5ufhdnihWEdHNHNSTuehmfw+twPPPP4958+ZhwYIF2LBhAyZOnIjp06fjUJRpQx988AGuuOIKXHPNNdi4cSNmzpyJmTNnYvNmVSR66KGH8Lvf/Q6LFi3CmjVrUFBQgOnTp6ND66zHJNFDIDVNRJfuWwsKhw6HAxPKJwAAPq7+2OTSJIchwmEGOtGZiR4Zu7YDf8Af/FwNmZVBJzrRklAHbKRBZiMEvEhCFmCMoBxLyG5oUH+Aa8WRI9Hd4KHPGf15h7q3Ul1E0gj3LaBfjIUUruTFXyhS0K2u1n6RqNZWNTMzUt3I8sjyaUm8Nijft5nHPnDA2GNrIYQZEW3EOJfMRK7F0a+fGgeQCFZxomeKgJvsgpZA+G8WPRZjTpRMmQ2QioiemwtUVorHZg5mBALqbz2j6yEnRxVw0410SWU2AKDN7EYtsEK8FEV04JFHHsF1112H2bNnY9y4cVi0aBHy8/OxZMmSiNv/9re/xYwZM3DLLbdg7NixuO+++3DiiSfiD3/4AwDhQn/00Udx55134qKLLsKECRPwzDPP4MCBA3j55ZcNfGfhBJSALlEdZjvRrSgcAsDE8okAgI9r7CUeGpmJ3tLZEsy1N5JMmpEBhM/KsBqyHext2osj7UdMLk3ihNajUW2BEM2wqpANmCcoFxWpLk+tjy3dt5Hc4IC+bsJYzl8pWHZ0pL4Ikt2d6FIoHjiw9/8GDBDT4gMB7XJGJfK7WFIS2ellBSFbj2PHirDR+9ix3res/3SyXLmwKEmVVKJcAOuJ6HbORG9sVPeRjIAr66C1VcRgmIUVZgPU1qY/kJCugGumCzo0htCMASWt2oKd6wAwd1CvokLcyzicbKWzsxPr16/HtGnTgs85nU5MmzYNq1evjvia1atXh20PANOnTw9uv3PnTlRXV4dtU1JSgilTpkTdpxG0dLZAgTjxaZqJflRgkgK9UVjZiQ4AkyomAbCxiG6A+xYAWjt1yMWNgx5rA8j31NbVBn8gzayyJLFyWyjxlmB46XAA9nKjy3bgcXngyfHodhzGuRBdsKqQDegn4Dc1qQJsvGNr/b4Tfc9NTeJC3qhjezzqRVeqIoydneiBgOp4luJtKC6X6m7TWtSV37FIMxNCy6OHC16+F/m9i3ZsPYRsuc9In7eex25pUZ2akY4t6zmdC18jnOh6DSalAUV0DUjFAQ1YR7TKBBd0aISFjJVLhPx8dSDUzAUVzYoRAdTfEd3dYuZdqiiKOisj2bagZ7+VKFK8dTr1/00UCZmLbrYTPZvPRxTRBbW1tfD7/SiXX8qjlJeXozrKh1NdXR1ze3mfzD59Ph+amprCbloj3bdulxveHO1cFHSiRyboRK/+GIqZ07+SRA+Xdk/ycvLgdIif5WaIhw2+BgD6LCwKGP+eLN8WKkRb2FS9ydyCJIERC+wCjHMhOhHPDWqmE12vY8v99e0b/Ye1Xtmmcn/RBNOCAnUKv9YXH/HqOt0f/Ea4bwF9xMPDh8VFv8Ohiqg90evCXIotkRzwgL4ueHnsaO9ZlkkPMcKsY8vjFhREFplkO0hVBPP7ga4u8dgIJ3pjo/kL6B2FIroGpCpaSRd0dbX6/TMDK7igtRLRk60DQD2fmSmim1kHHo96bkqnvzpwQCx67XIBw4cn91otBkLTJVS8NWM9PrNFdPl7ycx2AJjbFiiiW4uFCxeipKQkeBsSzT2SBnq4bwFmokfjuAHHweVwoa69Dgeadch+1AkjxEOHw2Hq4qJ65L57cjzwuIRjmW0hnEnlkwDYa1aGEbFGAJ3oRCfiibp6TRHv6lJ/3Ef7HSOPrXUmcizHsUSvCwD5YzrWsbWIk4h17GhibboXXUa4bwF9nOjyOzFggMhUjYTeIno0MVlPF3y8Y4e2Ay2FWr9fnWYe79hat/9471mrwSTAGCe6omg/ayVFKKJrQKqiVVmZOFcoSvoRDulghUz0urr0zlepzgYA1POKHms5JIqZTnQg/YFIQK2D4cOj98nRyPaBDEAbEV1RUh9QsoqAbOb5SNaB2Z+B2fTv3x8ulws1Pb6MNTU1qJBflB5UVFTE3F7eJ7PP+fPno7GxMXjbq8M0Cb0cxkERvZNO9FC8OV6M7j8aAMXDSJi5uKgeC4sC5g0oyTxtq7YFGW1EJ3pvzF5kl2Qo8sddNEFJPt/YGC4Opcvhw+IHusulLoDUE71+hMv9RfmdlbXHTveiy85O9HjRIqH/M1rINuLY0b4TFRXCRdfdre3iWIcOCZHL6VSn//fErM871MWYyuzQ0POknm3B41FFeotEulBE14BURSun0xqijRWc6H5/elEi6TjR9RoETwazBVwtnOCpLCra8/jZXAfyXJDOgNrhw+K3v8MBjBiR3GtDB1LMSllQFDrRrYDb7cbkyZOxcuXK4HOBQAArV67E1KlTI75m6tSpYdsDwJtvvhncfvjw4aioqAjbpqmpCWvWrIm6T4/Hg+Li4rCb1khxVGvh0PRMdIsKh0B4pItdMEw8NNOJrlPuuxnRHAEloIroVnWiHxXRPz/8OTr9neYWJkEMd6IzzoVoSTxhtbQUcLvFYy2jJORxy8vFxX8kZJm0jrCwspAd+j8t37eixD92usc1yokuRXQtnehWENFjfSfMEpRzc9WLcS2PLY8ro2oiEepE1/IiPN57lqJ+V1dqQpxsB7m50d+bVlhscVHdRfTHHnsMVVVV8Hq9mDJlCtauXRtz+xdeeAFjxoyB1+vF+PHj8dprr4X9X1EU3HXXXaisrEReXh6mTZuGr6R6ZwJ1derCzscck/zrrSDamJlBnJsL9OsnHmvhgra7gGtnJ7rdRXSzZwPIfiyd35GyHQwblvyAsPzd0Nlp3iBve7s6I8VMEb2mxryBBKswb948LF68GE8//TS2bNmCOXPmoLW1FbNnzwYAzJo1C/Pnzw9uf9NNN2H58uV4+OGHsXXrVtx9991Yt24d5s6dC0BEVdx88834xS9+gVdeeQWffvopZs2ahYEDB2LmzJlmvEUAIU50jYVD0923FhUOAXsuLkoneuqY0RZCF2a16oDS0JKhKPWWoivQhS2Ht5hdnISgE53YFp9P/XEbTcBzOPS5ME9GTD58WDjLzDh2pjjRm5pUh2yPdXiC2MWJLoVDOtHTo71d/QyNPnYi71n+z+dLz1Wa7LE9HnVdglTaoFHtAMguEf3555/HvHnzsGDBAmzYsAETJ07E9OnTcSiK1fKDDz7AFVdcgWuuuQYbN27EzJkzMXPmTGzevDm4zUMPPYTf/e53WLRoEdasWYOCggJMnz4dHVpOu0oCKRwOGiQy+5PFCiK62Q7cdAXUdCIstDi+Fpg5kAFo8z1MNdYIsEYdmN0OtIhzSacOvF7V8GDW+UjWAZDa+TRdrDCQYBUuu+wy/PrXv8Zdd92FSZMmYdOmTVi+fHlwYdA9e/bgYEiDPe200/DXv/4VTzzxBCZOnIh//OMfePnll3H88ccHt7n11lvxox/9CNdffz1OPvlktLS0YPny5fAa8eMrChmXiX7UuVnoNulElgDSiW6XGAtFUYL1qPX3pCdmOnB1jzYysC1I8dXlcGm6YLCWOBwO27UFOtGJbZE/rt1u9cduJMwS0cvKhEs9ENA2SsLqIroeU/LlvoqLgfx8fY6bCU70aHnxQOaJ6LKePZ7Y7d8sEd3jUaOezBLwUxFhjGoHgD7RRmmgq4j+yCOP4LrrrsPs2bMxbtw4LFq0CPn5+ViyZEnE7X/7299ixowZuOWWWzB27Fjcd999OPHEE/GHP/wBgLiQefTRR3HnnXfioosuwoQJE/DMM8/gwIEDePnll/V8K1FJR7wFrBXnYpYDN904ldpatT2NHJn8662QiW52HVglzkUuGG4GZteBFnEu6YjogPmDerIOCgqiz3jVk9BFdrM90gUA5s6di927d8Pn82HNmjWYMmVK8H+rVq3C0qVLw7a/5JJL8MUXX8Dn82Hz5s345je/GfZ/h8OBe++9F9XV1ejo6MCKFSswKtUvq0ZkknAI2CPO5YTKEwAAX9Z9aQuRrKWzBQFFTJHJVAeuoigZNaAUOpjkMGOl8ASxWy66HovPRoJOdKI5oYJurHOCWSK6y6VmnJrlgk81FzkS3d3qBY3RAn4y4j2d6JHRQ0xWFPNE9NDjxmr/ZonoZh5bToVPZfCOTnTt6ezsxPr16zFt2jT1YE4npk2bhtWrV0d8zerVq8O2B4Dp06cHt9+5cyeqq6vDtikpKcGUKVOi7hMAfD4fmpqawm5akU6MCGC+aAWY74JOV8SW4u2QIakNhFkhE93sOkg3zsXvB7ZvF49TaQuhi+xqHQeYKGbXQaiInupv2HRFdC0GU9LB7NkAQPrn5McfB84+G3jqKe3KRPRDt0z0o2Kr3L9RBEV0C8e5DCgYgCHFQ6BAwcbqjWYXJy5yoCXHmYO8HH3dNmY5cFu7WuFXRISA1m0h+J4MFETt0A4A4IQKMaC0/uB6k0uSGHrl5vdEzqSxwyAbsQmJCKuh/9dD1I0WLWLEsRMRlNvb1YuhdJGLqTqd0RdTDS2X3UR0OzvR5cV+LCe6/F9Li3bfiaYm9XOLVTd6XIwm2/61FCMSFdHlsdNx06VybJmrLPOpk4EiuvbU1tbC7/cHp35LysvLUR2lUVRXV8fcXt4ns08AWLhwIUpKSoK3IUOGJP1+oqGV89Ms4bC7W/3+my2ipyrgaiUcZnOUSLr91Z49IgLD7QaGDk3+9aGL7JpVD2bXgTSgpBMlotX5yKw6MHsgA0j/9/zatcCqVcC+fZoVieiIEU50xcCAfSk6WdmJDgAnDTwJAPDR/o9MLkl8QiMs9HY1myE4A6rD2OVwIT83yvT3FDHTiW71dnDyoJMBABsOboA/oGEOsk7oNejYE1lvPr8PXf4uXY9FsgSrC9lmHjs/X0SfaHlsuZ9YiymGlsssEb22NrUMeqOd6I2N6qJR6SJF2lhtobBQHSDQKl5IXlwWFcXO7JQXxFrGGiUqZKfjyk732PJ9Gy2iy0Gu2trk929knIseszLSwIQJ88Yzf/58NDY2Bm979+7VbN933AE8/TTQY9Z6wpjtRG9V114yPUokVeEunRiR0OM3N4dnMhuJ2YtapiueyjoYOTL1xZnNHswwO87F6wVKjup4qQyq+f3Atm3isd3jXMyqAyD9z0DWQarnI2IseolCUjjsDnSjo9u4NVvs4sA9eaAQD9cdXGdySeJj1GKKQEiMhcEO3NBFRbUeKDBDRLfDArsAMLrfaBTkFqC1qxVbaq2/uKjRcS4AI12IRiQqZFvBBavVsX0+1bVp9LETfc9SyK2rA7o0GjBL5NhyMUVFSU2UM9qJrijCyZ0ugYAqlkrRNhpaC8rJCtmpDnCkc2yzhGxA+887dJHSTHCi6zErIw10E9H79+8Pl8uFmh5qUE1NDSqinNQqKipibi/vk9knAHg8HhQXF4fdtGLCBGDWLGDs2NRebxXRKidHuIjNIN04lXTdt6EDomYLuGY70evrxTk3WdIdyAgtQza7oNP5DPbuFXWX6mwAwDrnIzs70WVbOOYYbcpD9CXoRNdYIC10F8IBIUbSgdsbuzrR9cYsJ7pe7QAwd2FRq7cDl9OFyQMnAwDWHbD+gJKe35NQ3C433C5xUcJIF6IJUjuwqhtcj2NLITA3V3VxRkPrRdoSfc/9+qnuK62ES3nsWMKh261ebEihMRmMEg+9XvUYWjhwGxrUxccSFdGNrBdAdUYrSmp1E4lkhWwtRfRE37fWAn7oYspy0CgSdnOiZ7qI7na7MXnyZKxcuTL4XCAQwMqVKzF16tSIr5k6dWrY9gDw5ptvBrcfPnw4KioqwrZpamrCmjVrou7T6pgtWoUKh2atvWS2Ex0wP1bHbCd6nz7qIEoqn0G6AxmA+SK6FQTcdNYUkXVwzDGpzwaw0vnILNK5jmhqUtsPnej2QK/FFJ0OZ9BNaWQuul2c6FI43F6/HfXt1vhBHA29viORMGtBRT1jOkzJRPfZox0A6qwMDiiFI783clYBIWlh9UgVPY4dGmET7yJfr2PHEw5D8zzNEPCB1By4RoqHWjpwpcu5pATweGJvq7Wom2i95OSooq9Wx5b7kSJ5NPSIkpH7indsrZ3ocj9lZbHbfzoiOjPR9WHevHlYvHgxnn76aWzZsgVz5sxBa2srZs+eDQCYNWsW5s+fH9z+pptuwvLly/Hwww9j69atuPvuu7Fu3TrMnTsX+P/tvXmYXFWd//++VdVV1d3pNVsnISRkIQsQAglpAigoGRLhp8MMM4ITRJ0YXAguMCr5jsIIIziKzIwMD6iAuMDE7cEBRoORVSESJyECIYQkJGTtbL1v1d1V9/fHzal7u7uWu5ztVn1ez1NP9XKrzr116txb9T7v8/4AMAwDX/jCF/Cv//qveOKJJ/D666/juuuuw+TJk3HllVeKPBRhsPNIV9fwaBVZ6CAcOguLeo2ONU1bRA8i4IqIwPKC6n4wjGCRLjwnMnhO/npBhygRtirDT5FdHhMZqkV01eMACDahxqJcxo+3o3kIvRGViQ5QFnQhGisbMbNhJgD9HbiyiikC6oTDkhsHIXGiA45VGYdCIKKriDaiOBeCB36EbB71THp6bIeIrpEq5dp2WGIseGZBsy/ZxVzoAH9xxG2MjMq2WbsdHf6W5o+kt9e6uWmb96SFU0QvRNgmkzrkGZMKIVREv/rqq3H33Xfj1ltvxcKFC7F161asX78+Wxh03759OOxQ7C644AI89thj+P73v4+zzz4bv/zlL/HrX/8aZ555ZnabL3/5y7jxxhtx/fXX47zzzkN3dzfWr1+PpIyTmABqauz3nQoXtA7CIRPR+/u9j4tDh6xzUzQKnHaa/30QsXrHCzr0Q5DPMDwEXNV9oIMLmocTnYeIrno1QFjHAY/JJEIuIh24ssXDjJlBz6A1Gx8GBy4TD3UX0UUKzCNRlYku0m2vcjJpTFzhBd0lzIn+lyN/wUB6QPHeFEZJtBHFuRA8cFtYlP3fz5fSXLAv98lk8Q+3vMVk1naxY3ZuwztKxo1gyrvtUhUPeTrRi7mindvIFnWd28huu77ecsID/pzZI2HP4YwPyoeoiQPmNM9HWJzozJ1WDiI6AKxZswbvvvsuUqkUXnnlFTQ3N2f/9/zzz+ORRx4Ztv3f//3fY8eOHUilUnjjjTdw+YiKnYZh4Pbbb0dLSwv6+/vx+9//HqcHUY0UYxj8rx1e0MH5WVlpXx+8indMODztNCvyzS8qBdxMRo9+8FtLZ2AA2LvX+jnIUBRRy8MLOvSBLk7048f51ffxgg59oHpFBiEXkRm/TGiSJR463cthcOBmYyw0d+DKKqYI2KKvqkx0IXEuJycGyImemxkNM9BY2YiB9ABeO/Ka6t3JSzqTzp5jyIlOhA634mFlpf0hlKeIVixSgW0D+BN1C7VdTEQT0TZ7HjeCKds/Hm2bpvvjDosTnYkksp3ovL+Ye3k/8hZm3LYdidjb8GibnXfGjXM//mUfc1gmk5iIzmMccEC4iE4UR2WEgg7uW8B/HjYv0UqliM5W+QB6OHC99sGePVbx7Orq4tF3hdAlUkdlH6h2oo8bx7++jxd0OB+xPjh61Jog8gKLc6GiouEgNZRC/5D1RUikA5eJsKJhAlfEiCAZ0391Xlic6CriXKQ70QU6jNk4kHlMbCyEYUWGYRihKLTrnAQhJzoRKlIp+wOmGwEviDNzJF6EQyZmtbZaX6xkts3zmJ3Pw45JVts9PXYMhyjxcGjILs4p04nOQzz040SX7YwG+GaTDw7a7mXZAr6fCJu+Pj4Zz27d9+w16e21RXG3kBOdUIlKEV0H5ycwPBfdCzt2WPdBFyOoFNHZ57pIRM45KB9+JzKc4m2Q4rQU5+LfiZ5K8VkNIKK+jxd0OB+NHWvX2vE6mUFO9HDhLPgpwrGaFdElFRZ15qEbqiqFe+DcSefCgIH9nftxpFtRVW8XSI2wUOS+FelEp0z04iyepP+EEhsHiWgCiViRgnQcICc6wQ0mkkaj7grW8HRG+xHRMxk+gqkfEV2FCz6IEzZfu8kkUFUlpl0mHLJ2RMNTPPSTic7bGS27bdZuJGLnyxeCp4DvZRxUV9vvJ57HXaztmho7wsbrWFDhRO/tVbNcfgQkomtAkEJ2QdHBfQv4F3CZiD5nTrD2VQq4zj5QqXswB+7+/d4ex0R0XqsBTpywJ/hlooOA6xTRMxn3j9u921rBWFvrzlxQCB1EdJXnI8MATjnF+vnAAW+PJRE9XDCHeG2iFtFIlPvzy3bgZoXDELhvAWs/546bC0DvSBepxRQVuW9Fxho5RXSTR6E+F2QnlEIyFs6bon+0kcxxAJATneCI0xUdcSF98HRGM1HKjYhWUWELRbLbZoIybye6Sud/sS/VTue/F5xu3bCK6Coz0WU70dl7orHRXm5dCJ7H7eWYDUONC94w/I9BmU702lr75055pox8kIiuASqL+ekgHAK2eFiOIroODmgAmDrVuvcrogddDdDYaH/e4fUZzgs6CLhsMmlw0NtkMK/VAADfzy1e0W0seBHROzrs14xE9HAgumCkbAeu04keFrLiocYxFiKLz46Eib59Q30YysibTRbptmfvx8HMIFLpFPfnz0XYnOisPsC2Y9vQM8BhGbcAZK7IABwiOjnRiaB4EZMBvoKyl1gTQIyg7CVShUeUjDMWQrYLXoYDngmH8bi7SZmg8BTR/RT3PHbMcmoFIZ22JytURaq4Hf88Y2y8uO+d2/Fo24uA7/ecx8a5DBG9osJy6wNaRLqQiK4BbLyoEA51Ea2YiO4lPmFgwMrjBoKL6CqLWuoykXHqqda9KhE9GrXP87IF3KEh+zORyn6oqLCv3V7GAq/VAIBaEV2XscCc6F7GAuuDiRPVr+wh3CFaHJUuoofMiQ4ASyYvAQBsOrRJ8Z7kR2ZhUafo6ywUKxqRcS6sWCqgYEIpJGNhSu0UTK6ZjIyZwZbDW1TvTk6kO9ET5EQnOKGDkO1VwJctKDc2WvemCbS1BWuX7XtFhbsP9DwnLVjbbvra72stUzgE1DnR2RdCZ6a4X9rb7SXWXkR0lUK2SgFfdtt+J7KYeCIjzgXQKhedRHQNINHKFnD37XP/mN27rYnNMWNsEd4vKqNE2ESGauGNuW87OrytkuElogPqVgR0O7QK1WPBT3HRt96y7ufODd6+DucjXcaCFyc6zz4g5CAywgJwiOgD5ETPx5IpJ0X0g5ukRX14RfT7xEkilkBFpAKAXPGQCaQiRPRoJJoV0mVHGzkFfN1xjgUdkbkiA7D7jpzoRGC8Clmq4lxUtu2Mkgkq4HuJVGHbOR/Hq+1isImDchIOvTjRk0k7QiPoF3PWbl2d9V4rBts/HlnHXhzZgLo4F0BMjI2bvg5DnAtgjwUeNSMCQiK6Bqhy3wL6iFZMRH/3XfePcRYVDRphMXas9Rymya+milt0mcgYM8YuAO7WgdvdbRfB5OGCVi2iV1TYRSVV4ccFXWoiuuqx4KcP2PmIRPTwIFI4BMiJ7oYFExcgHo2jta8V77S9o3p3ciI9xkJBQcXsMYqeUKJoo7zovipD5ooMgOJcCI6oFNFVtT00ZDvKZbft95jb2oJHyciIc5HtRGdfzoOK6E6Rw2vf8JxY8dJue7vc9wTgPyu/UNtuXfC8Xu9Mxltf+51QkllYFCAnOjEclXEuuohW06ZZ94cPAymXkZm88tABqygxO2eqEnBVT2QA3nPRd+2y7seNs8+/QVAVq6NLrBFgjwW3E0qmWToiui794KewKOsDHucjQg6Uia6eRCyBhU0LAejpwM2YmezrWsoFFUWPBXZMssYCi8IJ04SS7k500e+RkVCcC8ENHUR02VEyTgGwoUFu215fb/YFMpMJ7jD1I6L39tqOWjeE1Yne2WkL0m6/tPsVV0fiVUxm71nTDH7cXttWFakE2K93UAG/rc2Oz3Fz7vE7UaPKiU4iOgHYY5pHLQ+v6CJajRtnX4vcClc8Y0QAdS5oXfoA8C6ii+oD2QKuLpNJADB9unW/d6+77Y8ft66VhkGZ6LzwE+dCTvTwISsTXXaERZhEdMDhwNVQPOxKdcGEFTNTqk70wfQgegd7AZTGqoyMmUHPoFWcM0xjYfHkxQCAve17cbRHQYGeIoherTAScqIT3NBBRJedic4e39BgOcVUtO124iAet2NDZDqe6+qsglxe2w2rcMiE2cpK9xMAvFzZXsdBPG5/GZTdNi8hG/Ae58KrbXbM9fXu4nPYpIXXSSxyohMqYeenco4SMQzvueg8neiA+iiRMDrRS2UiQ6c+YCK6Wyc6c0BPm8bnGqZqZUwmA/RYuofyfmBO9CNHrALGxUin7bFAInp4KFkneojct4DtwP3zoT8r3pPRMOEwHo0jGZPzhVm2E50dI2C/Z3mTnVCSIIg6C7KGaSzUJeswd5x1AfnzQQ3Hguw4l5N9J7PALlGieBV1eYnJQeIzZLvBS6VtL31tGLZ46KWYqkrhMEjtGCbMelk6zsuJ7lVMBviNwyBCdpDX2xmp4tYFz0tE93rMzInuVUSXPaHEK9qIAySia0AsZp/DVTlwVYtWgPcYC1Eiuuw+0NGJ7nUig4cDGqDVAIA9Dtw60XkXtFTlRO/rsz+rqO6HceOszwOm6W5Cae9eS2xPJOzJQEJ/ZDnRpWeih8h9C9gi+pbDWzCYHlS8N8NxCodG0OIrLpHtRGfHWF1RjYqoC8eSD2SOBTb5EIvEkIgqLnLiEZ0jXZQ50SnOhQiKXyf6iRN2JIIfOjutbHJAfpxL2ER09vrIbtuPKKfKiT4w4C12ZiRBRHRezmi3YrKItr2K6Om0LRD4ob3dW6SKs+2gx+x18s6viE5OdEI1qtyfujjRAW9O9NZW+7UiFzQ/vK4G2LbNup8/n0/7FOdiO9Hd1gcQJaJ3dbmvT8AD9jnFMORdi/NhGMCMGdbP77iodegscsxWhhL6k3Wil0oxxZOi65i4BicyD8weOxt1iTr0DfVh27FtqndnGKLfI7mQLR7KOEY2MSBFRHdMJsma+OCFzsVFy6HALlGi+I1UCZrRzdqtqnL/wZa3kO1WvHNuq1LAl13AkolyXvpZtnBYU2N9MQGCiYcqRXTWryraZo93OxaccTdB3o9sdUN1tRVP4wbex+y2HkJYnOh+xqsgSETXBFXuTx0duG6c6Ey0mjKF376TC9oWcPfsKb5tOg1s3279fOaZfNpXVVhUp4mMsWOtz9uAu8kM3iJ6fb0dnyjzfMT6oLoaiGhwZfIiovPuA0IOzIEryonOxNBUOoXUkPgZqTAWUwSAiBHBeVPOA6CfA1e2cAgocKJLOMbauHwnetgmk4DhTnQzyDJyAWRXZZATnQgbfvKYWUZ3EEHZqxsU4Cdk+2mbt5DtRcDnMXlgmqXpRI9E7C+oskV0XpEqTFB2K+oCfItsqmg7TMdMTnTPaCBVEIA6EV0nB64XEZ2JVryiXAD1Aq4OfTBrlnW/b1/xLOh33rE+RySTwGmn8Wlf9WoAHfrAMLwVF+Ut4BqG/dlThYiuw0QGAMycad3v3l18W97RUoQcRGeiO8VsGYJoNhM9ZHEuAHDeZE1FdMnCIaAgE13wZBIgt8huWCeTAGDBxAWIR+No7WvFO20uZnAlosqJPpgZlDIJSZQo/f12wR0/om6QD8JBxOS2NsutpKLtoAK+V+evc9sgbff0AIMnI+HcCsVhcKIDfMRDJqyqcIP7EZR5CPhDQ7ZbUbaY7dUNDtjH3Ndnv8/8IEtED2uRXQ6QiK4JKkSrwUE7rkEH4YoJh26cn2+8Yd2fcQa/9lWJ6OzcrkMfNDVZLuhMpriAy6Jc5s3jF2HBRPTOTjVRIjqI6ID74qL9/faqAZ4CrorzkW594MWJ7hwLRHgQnYkei8RQVWEtK5EaYxFC8VDXLGglTvS4XCe6jDiXbLTRgNw4l7CRiCVwTtM5ADQcC5InlJwrCSjShfANE7KiUVuAcQMT0bwUnMzXthfRkolepilfMPVTZLNQ217EQ7ZtkJgG1m5Fhb2ktxhhcKIDfMRDlXEuqlzZzvcT62tZbfs55poaW1SR2bZTRPeyCo4J/SSiE6pQkYnOJuYBPYQrlm2+d29xAZWJ6Gedxa99XqvYvKKbC5q50XftKrwtEw55TmTU1VmffYDydkG7nVB6803rWjd2LDBxIr/2VayM0WkcAO6d6KYp5nxEiEeqeCgxxiKM4iET0bcd25Z1EuuAEie6ojgXkU50qZnoqfBOJgE0ocSIRWKojFlOT4p0IXzDxKT6ejtX2g1MVAoiKDMBz4uIFo/bAnAQQZk91otwyFtEl922Uzh029d+HLhhdaIHcUaHNc6FtVtTY2eVuoHHcfs5ZsNQI+CzcZDJuC+mapq2YCdrLPiZ9BIEieiaoEK0YmOkosJ9vQORTJhgRdCZZnHh6vXXrXueohWv64RXdHKiA95FdF556MDwa4fMftBNwJ0927p/++3C2732mnW/YIG37wbFUCmi6zIOnE70QpPy+/db1/JYjOJcwkTGzGQFPRkxFuREL8zkmsmYUjMFGTODLYe3qN6dLFmBOVEvrU1lhUVFZqKrGAchnEwCbBH9lYOvKN4Tm3QmnZ3cKuUJJaIE8SPoAnyd0V5ENOf2vARlt/iNdHCSyVjLib22zeOY/Uwc+IlzISe6d1SL6F7HYLm1nUzaYqDbseB0vJITnVAFiVaWCMjc6IXEwxMngMOHrZ95uqB5xdB5RTcB162ILiJSB+AXyecF3aJEWL45yzvPh1NE5wk50e3VAJ2dhSd02ITe3Ll6TEYS7uge6EbGzAAoIfEwxE50wBYP/3zwz4r3xKYsnOglloleKk70LYe3YDA9qHhvLJznLyXRRuREJ/zixw3u3F62qOvcnoeI7scN3t9vC8Ve6eiwnSeyBfwgEwdeRLmwO9H9iOidnXbevFcGBoDeXutnPy74chGyVbZtGN7HoDOzXXZh0SDnCU6QiK4JKgv56SJaAe5EdCbennYa3wkAds7KZOSOTd0mM5iIXmg1wNCQXUyRt4iuYkWAbn3ARPSdOwtP6IgW0ct5IqOyEpgyxfq50FgQ1QeEWJhwGI/GkYyJc1DIEtGHMkPoG7I+0IZdPNx0SJ8Yi/ZUO4DSFg5lHCM50d0zq3EW6pP1SKVTeP3o66p3B4C9IiMZSyIRS0hrl+WikxOd8I1fIYunkO1XwOcR5+I1jzlyUhrye9zscVVV3pwlPCctvBwzOdEL45yECfqeAPzVJZAdqeJsu1xEdMC7iM7GQSTiLSonCOz9099vTc4ohER0TSDRysKNiM6cnzxjRADrWs9EVJkCrm794MaJvnOnde6qrgamTePbvkoRXZc+OPVU67PZwED+Aq+mCfzlL9bP5EQXAzsfbd+efxsR0VKEeJwRFgbPLKQRyBIPnTniYRUPdcyCLicnushjZO9JWpFRnIgRwXmTzwOgz1jIvkckTiYBjrFATnTCL2GLVFHZdiQS3OkZ1H3Pw4nupW1yohcmFrNfI7+iLuuXujq7aKYbVArZKl3wqtr260SvrOSbKVuI2lr7Z8WRLiSia4JTtPJSFDcIOotWzOWci61brXsRzk/ZxUUHBuyJNF1c0KwPdu/Ov5pv82br/uyzbdMCL1SI6LpNZEQidr52vkiXI0esSbdIBJg/n2/7FC9lwYTx1wuYAZkTnUT0cCGjmCIgT0RnIlNFpEKqU5QniyYtggEDe9v34mjPUdW7A0B+MUVAvhNdxlig2gDe0G1CSUYR5lxkxwI50Qm/qMxEVxXn4swl93vcQV3HQdz3foUQWTnwYXWis9fHi4ju3N7vF/Ogjuy2Nus9raLtMLrgTVOuE13mOIhGbbGGRHQCsMXbwUH7uicaHUUrNyL6ppPfKc47j3/7TMCVtSKg2zYPaiPgTp5snb/TaeDNN3Nv8+eTkbUi+kBFJrqOE0rFRPRXX7XuZ8+2Vk3yRGWhY536oJiI3t1tu9TPOUfOPhF8kCUK1cYlieglIBzWJeswd5yVZaVLLno5ONFlFhbtGujK1iIQRdjjXAD9RHQVk0kAOdEJDqjMRFcV5+I3l9y5vSoRPZ0e/uXYC7IKi4bRid7XZ4uefkX0oE50v2Kyafo/bh0iVWS/3r29dn69LCe6TPysHhEAieiaUFVlC2GyhCtdhUPDsF6DI0dG/7+nB9i2zfp5yRL+7ct2orM+SCblxUkVwzAshzlgx4WM5P/+z7pfvJh/+5SJbsFy0fNFifzpT9Z9czP/tilSx4JFRuUT0TdvtswRp5xiTT4R4UFGMUVAXkFF9vwsQziskHiowIkusbAoAPQM9AhrByiNscDiXN489qYWArKKySSAnOiiuO+++zB9+nQkk0k0Nzdj0yZ359t169bBMAxceeWVYneQJ2HORJedSw4Ej1Xx675PJu19lSng+xHkwuhEZ4JsLOb9i5YqET0et3JjVbStMkqGbR/0mGMx+/VzQxic6ACfVRkcIBFdI2QLuDo6P8eMsZy1QG4Bd8sWS7SaMgWYNIl/+7LFQx37ACgsog8N2S5okasByjnOBbALtrK4kJEwEf388/m37Yxj87uCzis6TmSwPmhpyb0y4pVXrHsRExmEWGS4bwFHjMWAJCd6iN23gC0e6lJcVNZkixPmvh3MDGIgLb5wkoxVGclYElHDykKlVRnFmVQzCVNrp8KEic2HN6veHXVO9JPnM2fNByIYP/vZz3DTTTfhtttuw5YtW3D22Wdj+fLlOHq0cITW3r178U//9E94z3veI2lPOVGOcS5+2wXUOdENQ03bTJDr63NfqDCMTnQmyDY0eM+vDur89fueUNk2z/gcvxN4PI7ZS1+HxYketG4DJ0hE1wgedQS8oKPzE7BjEZhQ64SZJUS40AH5cS5MvNVJOAQKi+jbtlnnzdpae8KDJ7L7ANBzLCxaZN2/9pq9KouRydhjQYSAyybgnbGKotGxD2pqgNNOs35+443R/ycRPbyUWiY6E5nCLBwCw53opqwCMXlIZ9JZQVamA9fpoBbtQjZNU8pYMAxDen2AsE8o6bQqgwqLlg733HMPVq9ejU984hOYP38+HnjgAVRVVeHhhx/O+5h0Oo2VK1fi61//OmbMmCFxbzmgKs4lnbY/QMuOcwkiWqoS0QE1Lng/hQrD7ET3Gi0CBO8Xv7EmPNv2K2SnUvkLxBUinbb7ym/bqo7Z7dgnJzqhCzxqGHhBR+cnACxcaN0XEtFFOKABdXEuOgmHgC2ib906ur4Lc0AvWsS/qCigprgrE6l1GgszZljXs1TKjjBivP22dW2trBRT0DKZtOOlyn1VBnt9WUFjJ0xEFzWpR4hDuhOdhENXLJi4APFoHK19rdjTvkfpvjj7TKZ4GIvEUBmznD2iYyx6B3sxlBkCUEJjoQSc6IBmIjpzopdwnMt3Xv4OTr/3dHzzj98U3pYqBgYGsHnzZixbtiz7t0gkgmXLlmHjxo15H3f77bdjwoQJWLVqlYzd5EtQQam7e7STxQ1OgUe2Ez1sQjZDhYAfjdpCulcHLono7gjyfgzqOvbbdk2N7eL285o7H+PXBS/7mNk4cOueU+1EJxGdYJAT3YLlbDOxlmGawPPPWz9feKGYtlXFuegk3gLA/PlWlFZ7O7BnhI7x3HPW/cUXi2lbVR8A3qLDRGMYwLnnWj//eUSNvZdftu4XLQIqKsS0T+cjC+YyZ685Y88e4OBB6/M3WzVAhAdZMR1MyCPh0B2JWAILmxYCUC8eMuEwEU0gEUtIbVuWA5cdY8SICM8QdxYXFUl2VUbIJ5S0EtFVO9EliOgHOg9gZ+vO7ARrKXL8+HGk02lMnDhx2N8nTpyIlpaWnI/54x//iIceegg/+MEPXLeTSqXQ2dk57KYMv3Euzu39iFms3epq7x/UgzrRwyZkM1SJtV5FOebADWOcix8RPWjbYYxziUS8T67katfP+Fd1zOx4u1xeb8mJTuiCbCe6rs7P88+3hKl337VujDfeAI4etRyyInKgAYpzYSQSttv/hRfsv5umLaK/731i2mZ90NHhz/zhFSbeJhLiBGm/sPf5H/4w/O+/+511L6oPAFoZw7joIuv+j38cvipjwwbr/vzz9TuHEsX5f+/5f3jx4y/i2gXXCm2HnOjeWTJZD/FQVTFFQJ4D1ymOGl5zUj0ibUIpVRoTSosmLYIBA/s79+Nw12Gl+6LciS4hzkVWxFeY6Orqwkc/+lH84Ac/wDi2TNQFd911F+rq6rK3qVOnCtzLIvgVlGIx+wNpEBEtbJEqYW47aAZ9GJzoAwP+4kXCGKkS5rZ5ue/9xBoGcd8D+ovoQScZOEEiukbIduDq6vwcM8Z2ozvFQyYcvuc93ouNu4XiXGyYQMvc/4AVaXH0qDX5LioH2lkHQ4YLWlfxFgDe/37r/pln7OtoOm2PheXLxbVN5yOL886zJlcOHwZ277b/zkT0yy5Ts19EMKbVT8N7pr0HpzWcJrQd6REWpSCia+LAVVVMEZDnRJdRVJQhYywMZYbQN2QJHGEfCzWJGswfPx8A8OdDfy6ytViUFRaV6ESXFfGlknHjxiEajeLIkSPD/n7kyBE0NTWN2n737t3Yu3cvPvjBDyIWiyEWi+HHP/4xnnjiCcRiMex2fihysHbtWnR0dGRv+/fvF3I8RRkcBHp6rJ9li7pB3ODOOBeZIpqz7aAu+CCvt5+2BwaA3l5/bYfBie78kupHPHQWFvVKWONcgtQlAIKJtTzGYNAJE4pzEQqJ6BqhKj5BR/GQRYX87//af/vlL637yy8X1y7Fudhccol1v2GDdR0CgF/9yrpfscJybosgGrXP+zL6QdcVGQBwwQXW63zoELB9u/W3P/7Ruj7W1YktaCn7fKRrP1RWWv0AAE8+ad339AC//a31s8iJDCL8SHeih9x9C9gi+pbDWzCYlrAcKQ+yIn9yIc2JLtF9K2MssCgXoLTGgvIJJUWrMmQ60ZmIXspO9Hg8jkWLFuGZZ57J/i2TyeCZZ57B0qVLR20/d+5cvP7669i6dWv29qEPfQjve9/7sHXr1rwO80Qigdra2mE3JTiFtzof790g2eQ8HNlDQ7Yw7AUd4lyCTh54hR2zYXjv6zA4cKNRez/9iIc8JnVUusGDHLPzefy0LduJPmZMsDz2UneiB82M5wSJ6BqhKj5BN9EKAK66yrp/8knr88PevVZGumEAf//34tplTvTjx/1N/ntFZxH9ve+1rh+HDwMvvghkMsDPfmb97+/+TmzbMiczdB4HlZXApZdaP//3f1v3P/6xdf93f2etNhWFzPNROm1/T9CxH/72b617Non0xBOWkD5jBhUVJQrDhMPugW6kM2lh7ZSSE3322NmoS9Shb6gP245tK/4AQch0aY9EuhNdgvu2Nn4yE13gMbHnjkfjiEcFLVmUCBPRXzn4itL9YO+ThqQPQSAAMp3o5RLnctNNN+EHP/gBfvSjH2H79u34zGc+g56eHnziE58AAFx33XVYu3YtACCZTOLMM88cdquvr0dNTQ3OPPNMxEUtC+YFE5Nqavx9YA4iKAcR0aqr7f1VJeCHLc6FPaa21sqz9oIX8TCdthzCgHwHbhBBmT3Gz2SSDpnoQYTsMWP85bUGEWuDHHMkoqZtcqJ7gkR0jSDnp8155wGnnWYJVT/+MXD33dbfL70UmDRJXLusD4aG3E/EBUFnATeRsCcs7r8f+J//AXbtss5dH/yg2LZlZtPrvCIDAD72Mev+kUeAd94B1q2zfr/uOrHtypzIcBptdOyHv/1bawLvpZeAzZuB73zH+vs//INtFiglWltbsXLlStTW1qK+vh6rVq1Cd3d3we1vvPFGzJkzB5WVlTj11FPxuc99Dh0jPuAYhjHqto69oUsUJqIDw12yvCmVwqKAVeTyvClWUQ6VDlylcS6SM9FlCIcyMtFLaTIJABZPtrINXz38KkwZzo48tPVbX8plC8ys2K1MJ7qKSTOZXH311bj77rtx6623YuHChdi6dSvWr1+fLTa6b98+HD6sNoOfG0GiRZyP8yNkBXH+GoY6F3wQ0dI0g73mqpy/XkT0VMr+OUwFFYOI6GGNcwnSLqAuzkVV285x4ObzhmonumIRXaCPkfAKOdFtDAP4wheAz3/euh8asv7+z/8stt3KSuvW12f1g+gViDo70QHgs58FHnwQ+MUv7GidG24Qv78yBVydJ5MA4EMfsiaODhwA5s2zDBBLl1q1AUQic1KP9UEkIv9a7IZTTrEE80cftWKOurut98uNN6reMzGsXLkShw8fxoYNGzA4OIhPfOITuP766/HYY4/l3P7QoUM4dOgQ7r77bsyfPx/vvvsuPv3pT+PQoUP4JcvhOskPf/hDrFixIvt7vZ8vmCEiEU2gIlKBwcwgOlOdwgQaJjIx0SnsLJq0CL9/5/d49fCryvbBWXRTNrJiLEotE72UYo0A4IzxZyBqRHGi7wQOdR3ClNopSvYj60SvlOxEd0wmmaYptPhtOcS5MNasWYM1a9bk/N/zzkJIOXjkkUf475AoeAlZsoVs9rjjx+UL+Gx/Ozst13U06v6xvb1WDn3QtoPEuQQR0d04cJn7FgiXeMgrzsU0vbmHgtYlUDWxorptlU70TMYay9XVhbcnJzqhC5SJPpzPfAZYsMCa9E2ngQ9/2M7pFokz0kU0uovoCxfaQmFvLzBzJvClL4lvV2aBV50nkwDrM9r3v2/9PDBg/X7vveId0DIn9Zx9oKuz+/bbrc9TbF//9V+BCROU7pIQtm/fjvXr1+PBBx9Ec3MzLrroItx7771Yt24dDh06lPMxZ555Jn71q1/hgx/8IGbOnIn3v//9+MY3voEnn3wSQ2wG9CT19fVoamrK3pI6zppwxDAMOeJhiTlwz554NgDgL0f+omwfVIpqWQeupEx0KXEubBwM0DhwS2VFJeaMmwNA3VjoH+pH/5DlOJM9FthkSMbMZAvGisA0TaWTZoQgguRzA+pEXUC9Ex3wLuCxdmOx4gJcLoI4/4P0tRcnOnPfxmJiMzVzodqJ7szedIvz/RukbRVucB1c8DLbrq62v4R7GQuyv8cFeU9whER0jWAiekeH7bwWie7iYUUF8NxzwLe+BTzwAPDTn8ppl/K4h3PPPcBDDwF33gm8/LL/z6JeUNEHuk5kAMD/9/8BzzwD3HqrVRtg0SLxbVIfDGfGDGDTJuC224Bf/9paJVOKbNy4EfX19Vi8eHH2b8uWLUMkEsErr7jP5e3o6EBtbS1iI75k3HDDDRg3bhyWLFmChx9+uGBEQSqVQmdn57BbGJHpwHXGx4SZs5ssEf21I68hY2aU7AOLsJDtvgXkZaLLjHOROQ5KZUUGACxsWggA2NqyVUn7bDLJgCH9/OLsR5FjoWewB2nTqllRDk70soGHG9z5PH7aDpuAX1Fhfyn1K6I3NPhzxKiaOPAioqty3wLqRPSqKntFgtf3BNu+ttbbqgZGEDE5iPve+TjZzn+AT197bdsw7LHvZVVGmTrRKc5FI5zv9dZWsS7HgQG7LobOAm5joxznsxOZLmjdneiAdc37x3+U26bMTHTd41wY73+/dZOFzJUxYZhMAoDZs4F/+RfVeyGWlpYWTBhx8YnFYmhsbERLS4ur5zh+/DjuuOMOXH/99cP+fvvtt+P9738/qqqq8Lvf/Q6f/exn0d3djc997nM5n+euu+7C17/+dX8HohFSneglEmNx+tjTkYgm0DPYg92tuzF77Gzp+6DSiS4rE7091Q5ArhNdaGHREhsHgLUq47HXH1PmRG/rs8SpumQdIoZc71XEiKC6oho9gz3oGujCREwU0g4b67FIDFUVVULaIBTAyw2uwonq15VtmsEFfLbs0quYHVQ4VJVB78eJrmIVpSoRnWX0nzhhvc5TPMSK+RV0GUHc4EGOGQg2/pkIraLtIMddW2uNA53HAjuuVMraB0UrmsmJrhGxmD1mRAtXzhpxugtXslEh4OosoquAVgOoR2acS1gmMsLMLbfckrOwp/P21ltvBW6ns7MTV1xxBebPn49/GTHj8LWvfQ0XXnghzjnnHHzlK1/Bl7/8ZXz729/O+1xr165FR0dH9rZ///7A+6cCqVnQJRJjEYvEcOaEMwGoi7HIOtGTCp3oJVRYlMaBP3RxoqsYB4A9FkQWZnZGuYjMXSckE1Y3OODfle3MJZftwOcVYdHbazv9ZLTNsqBL1YmeydjHJlvUZfvqt8gca7ez0zoOP22rFLKDHrfXtgcH7cgdP22HYVVGTY290kWhG51EdM2QJVwx4TCRsFZuETYk4KpHRSY6TWQMR2a8FI0D8dx8883Yvn17wduMGTPQ1NSEo0ePDnvs0NAQWltb0dTUVLCNrq4urFixAjU1NXj88cdRUeTi0tzcjAMHDiCVSuX8fyKRQG1t7bBbGMk6cAUKoqXqwAWAv7SoEdFVFVMEqLCoX0p5HOw8sRM9Az3S22eTSapiTmSMhXIqKlpWlGOcCxPdolF/ueRB2g4qWtbV2eKYzLbD5kT3Kqx2dlorFJzP4RW/0Sa8HNmZzHAHqIy2g7jgebXt9fV2vof9fGdij3ET56JqLEQitmDgZswKguJcNGPsWOCdd+Q50Um0Gg3FuaiH4lzU4/zO0dYGjB8vri2ayBDP+PHjMd5FJy5duhTt7e3YvHkzFp0M33/22WeRyWTQ3Nyc93GdnZ1Yvnw5EokEnnjiCVcFQ7du3YqGhgYkEgn3BxJCRIuHqaEUBtKWa6uUHLgsF111jIWSOBdZTnQVhUUFiujMrVxK42DimIloGtOElu4WvH70dZx/yvlS21c5mQTIGQsyJ5MIifAQdQF3glK+toPGWHht2yne+V1V4UVIcxL09Y5ErC8CnZ3WbaKH+KYgomUY3LeAf2GVbZ9I+Bc8/QrKQR3ZySQQj1srE9rbvT0PLze47PgcZ9t+X+/KSn8uWT9jQcWEEoudUVgri5zomiHLBU3CYX4ozkU9tBpAPbGYfe2n81H5MG/ePKxYsQKrV6/Gpk2b8NJLL2HNmjW45pprMHnyZADAwYMHMXfuXGzatAmAJaBfdtll6OnpwUMPPYTOzk60tLSgpaUF6bRVrO3JJ5/Egw8+iDfeeAO7du3C/fffjzvvvBM33nijsmOVhWjx0CkulaIDtyzjXCQ50WXGubBjojgX76hclaFyMgmQMxbYZBI50UsMXiK6HxGNiTt+BTxVQrbzsUEEfL+w4/YrFAcR0XV23wLBRfQg/RJU1OXRtioXvIo4F1V97ceJrmJCyYvYLwhyomuG7DgXEq1GI6ugYjptT+JRPwzH2QeZjGVOEAW5oPMzdqx1PabzUXnx6KOPYs2aNbj00ksRiURw1VVX4bvf/W72/4ODg9ixYwd6T+bubdmyBa+88goAYNasWcOea8+ePZg+fToqKipw33334Ytf/CJM08SsWbNwzz33YPXq1fIOTBHCRfST4lJlrBKxSOl8rFswcQEAYF/HPrT1tUl1wmbMTFZgVhLnIsmJriLOJZVOITWUQiLGfwVKKca5AFYu+tO7n1aSi65LJroMJzqJ6CVGUBHNr5A9MGALTLJFtKDivfOxKtquqwMOHPAv4JdqDjTgLbvdSZiFbNb20aPyXfBsn7u7rVzTmMvP1+m0/cVWtoAf9PUOS7SR33MzR0rn21aJIEvAJeEwP2wiQ2ZxV+qH4bBxkMlY10C/cYZuIBd0fmTHS9E40IPGxkY89thjef8/ffp0mCxfEcAll1wy7PdcrFixAitWrOC2j2FCtAO3VIXDhsoGnFp3KvZ17MNrR17DxdMvltZ2Z6oTJqz3tJI4Fwnu23QmnX3vSHGiO96fXQNdYkX0UnWiK1iVoVpgluJEdxQWJUoIXm7wVMq6uY2ecwo7qpzoQYXsIG3zcKLLbJt9+ejutrLDC8XghFE41MGJzuP9KNsF73xcZ6ctEBUjaC45oO719vIeUzmh5GX1iCAozkUzZDnRSTjMjywRnfVBRYX7z2XlQiJh18Oh+gDqoJUxBBEcWU70UhMOAXXiIYuwSMaSSMbkf1l2um+LTVD5xfl+lCEexiIxVFVUjWqbJ9mxUGITSmdOOBMAsO3YNmHvh3yojDUCHCI6OdEJr/CKNgC8iTVs2+pqq8CnH4I60VUI2WF1wTNBLpMBTq6wzIsOwqFXJzoTYnlE/JRTnEtFhS1GeDluZwa9X4FHVZxL2JzoCuNchInora2tWLlyJWpra1FfX49Vq1ahu0BV3dbWVtx4442YM2cOKisrceqpp+Jzn/scOka8eQzDGHVbt26dqMOQjmwnOolWowlSjN0L1AeFkZWLTi7o/FCNBoIIjqxM9FITDgFbPHzz2JtS21UeYXFSOMyYGfQN9Qlpg+VAJ2NJIa7wXEgbCyU2oXT62NMRNaLoTHXiYNdBqW2rFpizE0oCnejZWCNyopcWQUXdaNQW0byIWTzctyqFbJXZ20Fd8H6Ou7radp8XE+V0EA79vjZ+i9w6H+s3XiTI+1GlC95P27wibLy2y6PtsEUblaITfeXKldi2bRs2bNiAp556Ci+++CKuv/76vNsfOnQIhw4dwt1334033ngDjzzyCNavX49Vq1aN2vaHP/whDh8+nL1deeWVog5DOiQcqoe5bzs6rAgsUVBR0cLIXhFAAu5oaFKPIIIjWjhkz1tqwiEAnDH+DACWA1cmzH2rSjisjldnfxYlHqoQDqWtyiixCaVELIHZY2cDALYdVTMWVNQGAIAxceuDgUgnOhUWLUHSafsDvmxRV6UbPOwCvp+2M5lgfW0Y7sVDHYTD7m7rmN2i0g2uKkrGNPlO6viZROPRLstj99q2jDgXlRNKpVpYdPv27Vi/fj3+/Oc/Y/HixQCAe++9F5dffjnuvvtuTJ48edRjzjzzTPzqV7/K/j5z5kx84xvfwLXXXouhoSHEHGH+9fX1aGpqErHryqH4BPU4J2rb24Fx48S0QyJ6YWSMBdOksVAI2ecjGgtEKSJLOGTtlBJnTDgpoh+1YiyMQlmlHGFxLqqEw4gRwZj4GHQPdKNroAsTMZF7G9kcaAlFRRnkRPfPGePPwFvH38K2Y9uwfNZyae0qd6JLjHORORYIwThXvwcVdQ8d8iei83CD9/YCg4NWtISXtnkI+H4FU9lxLj091he6IG3X1FivXbF+1kE4BKz3t9tjVZmJzuP96EfITqWscQPId6LzzIEHvOWxy3Kim6YeE0ql5kTfuHEj6uvrswI6ACxbtgyRSASvvPKK6+fp6OhAbW3tMAEdAG644QaMGzcOS5YswcMPPyw9H1Akspyf5L7NTyxmn3tE9gOJ6IWRMRb6++3JfOqH0cheGUPnI6IUoTgX/8wZOwcRI4K2/jYc6TkirV3VcS6A+IKKKty3siaUmHu5lMiuypDtRGcTSqqijSTEuajOfScEwISseDyY4BnEicpDTAa8uS1VFhblKZh6aZsdc0WF/7726kRXIaInk5ZIAfh7T4S1sGgQIdu5ykBW2zzGQTxui9N+jjtoHYhi429w0J64ClO0EUeEiOgtLS2YMGHCsL/FYjE0NjaipaXF1XMcP34cd9xxx6gImNtvvx0///nPsWHDBlx11VX47Gc/i3vvvbfgc6VSKXR2dg676Qo50fVARpQI9UFhZIwF52eQqipx7YQVmtQjiOBQYVH/VFZUYkbDDAByxUPVcS7A8OKiIlAZ5yJKEC3lCSVncVGZlIMTXfXKE0IAPNzgzsfLjnOpqLBFtLBEyaiKc3G263e1mlsRnTnRVbhvnYKwl9cn7IVF/bTN2q2pASIB5M4gbQc996hwwXudTALCVWSXI57eVbfcckvOwp7O21tvvRV4pzo7O3HFFVdg/vz5+Jd/+Zdh//va176GCy+8EOeccw6+8pWv4Mtf/jK+/e1vF3y+u+66C3V1ddnb1KlTA++jKJho1dtrn6dFQPEJhZEhopMTvTAyBFw2DqqqrPpBxHBoUo8gguMU0UWsnCvlCAtATS66avctIN6JriLCQuSE0kB6AAPpAQClORZYtNGbx96UtgI3Y2bsVRmKBGZyohO+4CHeOR8v24nufLzstv0cM68M6iDHHKTdMDjRAfv1ke1EZ4/1KlryjDaSHankfLzs19tv20En0dxOYjlFynjcX1tBCJsT/eabb8b27dsL3mbMmIGmpiYcPXp02GOHhobQ2tpaNMu8q6sLK1asQE1NDR5//HFUFMkAa25uxoEDB5BKpfJus3btWnR0dGRv+/fvd3/Qkqmrs8U8GQIuiVa5aTj5ObqtTVwbJKIXRuZqAOqD3FChY4IIDhMO02YafUN9Rbb2TrawaAm6bwFbRH/z2JvS2lQtHALynOgyhcPauDgR3SmyluJYmN04GxWRCnQNdGF/p5zvMV2pLpiwBHvVTvTuge4iW/rDNE3lbntCAGF3ojsfL1s8ZI/t6bEKtLqhr8/eNoyCaRic6IA/JzoTdZ1F3/y229vrvtBlOm1/wZMxweGEt5CtYvz7aTvocfuZTJJUq2gYGojongqLjh8/HuPHjy+63dKlS9He3o7Nmzdj0aJFAIBnn30WmUwGzc3NeR/X2dmJ5cuXI5FI4IknnkDSxSzf1q1b0dDQgEQikXebRCJR8P86YRiWgHv8uCVc5ajBygUScAtDcS7qkSHgUh8UhuJcCCI41fFqGDBgwkRnqhNVFXyzo5jIWoqFRQFg/vj5ACQ70TVwpooWD1W47UU60dnrlIwlEYt4+noTCiqiFTh97OnYdmwbth3dhlPrThXeJhsHyVgSyZga56XoyaSewR4MZSxRiOJcSgiVTnTeAr6ftnm4wdnzNbgYF2wfIxGgujp427Jfb68ierk50Z2CUVeXu/eEcx9VRfwEHf9BJi1UTOAFbdvZpmnmF8h1mUwKS5yLW+bNm4cVK1Zg9erV2LRpE1566SWsWbMG11xzDSafVIUPHjyIuXPnYtOmTQAsAf2yyy5DT08PHnroIXR2dqKlpQUtLS1In5zZfPLJJ/Hggw/ijTfewK5du3D//ffjzjvvxI033ijiMJQhQ7jidX0vVSjORT0y+4DE29ywPhAZLzU0ZD839QNRikSMiNBIglLORAfsGIttR7dJi7HQwZkqOsYiO1EgUTjMiugDApzoJR5rBDjGgqQJJS3GgeBYIzaZFIvEUF0RQPwj9EKlEz3MIhowvBir27Z55JKzx3tpF1AT56JaPJT9+vh5T7B2Ewnr5hcdxqDMSBWGH6E4aNuszUxmeO75SHSZTAqLE90Ljz76KNasWYNLL70UkUgEV111Fb773e9m/z84OIgdO3agt7cXALBlyxa88sorAIBZs2YNe649e/Zg+vTpqKiowH333YcvfvGLME0Ts2bNwj333IPVq1eLOgwlyCyoSAJubkhEVw850dXD4qXSaasfpkzh3wYvowJB6ExtohadqU4xMRYlXEwRAOaMnYOIEUFbfxuO9BxB05jCsYA8UCEwj0R0QUUVbnuRTvTsZFKJjgNAfn0ALWoDJOwVGaZpwuC8dNwZa8T7uQmF8I5zkO0Gdz5eVZREf7/74+Y1aaE6wsZtFrRq8dCLsMqjsChru7/fu4iuQkxWGecS5radq0i6uqyicbkI42QSZ4SJ6I2NjXjsscfy/n/69OnDHEWXXHJJUYfRihUrsGLFCm77qCsynOgk4BaG4lzUQxMZ6jEMqx+OHRMvosfjamqTEIQMpIiHJerArayoxIyGGdjVugvbjm6TI6LrIB5KcuAqcaKLnEwq0XEA2NFG249tl9KeTk50EyZ6BnswJs73QzObTKI89BJDpairqrCoM4Oax3EfPSpfMHUbJ8G77VJ1og8NWdn2AJ++8fKeELEaxO17glfbQQR82W2bZvDjjkSsdru6rOeaODH3djpNJrl9T3BGSJwLEQwZDlwSDwvDor5IwFUHGwft7e7r2niFYo2KwyYzRBXZZeOA+oAoZUSKh6VeWBSwHbjbj5eReCg4C5qc6OFjztg5AIAdJ3ZIiTbSYUVGVUUVIob1dVXEhJKKySRCAjo40WUL+DyXdnp1wfIWTNNpK0tSVtulmonu7D9eY8Ft27yd6JmM+/dEORYW7e21BRMeE0qF2lU9mcReG9O0J4kkQyK6hoh24KbT9vuNBNzciBYOARLRi8EmMkzTXonGG+qD4og+H7FrNPUBUcowN6VIB26pFhYFHOLh8R3C2zJNUwvxUHicCznRQ8esxlkwYKC9vx3Heo8Jb0+HySTDMLLucxFjwRnnQpQQOsSLyBbwWbtBM6gB7wVVeYmW1dWWGxaQ64IvVSc6O55EIvhyX69t83xPMKexbAE/THEurN1oNH8MixtYREIhcVr1ZFJlpffzBGdIRNcQ0aIVW+kFkPszHxTnop6KCvt6LWpVBonoxREdL0V9QJQDFOcSjNPHng7AcuCKpm+oDwPpAQB6ZEELLyxKTvTQUFlRiWn10wDImVDSIdYIEBttpMOEGSEAHUQ02W5wXu36aZvXpIVh+J88ICf6aHiKDTq8J2S3HaY4F2e7QeJN3IjoqieTnO8JL33DERLRNUSWaBWLBZ+oLlUoj1sPRI8FinMpjuhCxzQOiHJAlHiYMTPoGbQ+6JayeDhnnB1jIRrmTI0aUe75y14Q6UQfSA+gd9BaFk1O9HAxd9xcAMBbx98S3pYOTnTAPreJeN+wiYL6RD335yYUwtuJ7lbQdeYSyy4syvNLjdfj5tm2CsG0VJ3oTETn8SXL76RO0HEA+BeUZU+i8Rz/fscBD+c/MNx1OxLVk0mAvwlOjpCIriEyRSsqRJ8bp4guKnaSxMPiiJ7MoD4ojqw4F5rIIEoZUeJh94D9IbeUxUMW57KvY19W/BVFVlRL1sNQ+CFJpBOdiaMAUJfg8AXXJWwcdA90I53hW+yEvU4qJz5k4MxFF017qh2Aeic6e492pDzkUruEnOgligg3uJsvhP39wOAg37ZlR6o421YhmHqdPOAZ51KsTdXioVf3LdsuzE50lW2z98XAAJBKFd/eOf55ZcHLnjgIgxMd8D6hxBkS0TWEhEP1sDzudFrcKhGKcymO6CK7NBaKQ+cjggiOKBGdPV/UiCIZU+gIEcy4qnFZIW/niZ1C29JFVBPpRGcTBbWJWkQjUe7Pnw/nagnnBBAPysWJLlNEd04oqYS139HPX0SnTPQShbcb3G2hS9auYQT/gqfSia4iUkVl226EQ0C9eOjXia5CRBfhRJfdtvPLqRtBiLVrGLaj2y+q4pzC5kSnOBeCQc5P9VRW2ucFEf2QyfBdYVWq0FhQD4noBBGcrIg+wFdEd+ZAq3RNi8YwDGmRLrpFWAjNgZYsHCaiCVREKgDwn1DKiuglHGsEOKKNJGSit/ZZF37VE0p1SfFOdNXjneAMT0HJSwE7tk1Njf04v6iKc3A+hwrBVIVYy4TDQiK6adpu5LA40UWI6LKd0Srbjkbt94aX8V9bG3z8e3Wi8xr/5ER3BYnoGkKilR6wfmhr4//czvMS9UN+yImuHlnnI5rIIEoZUU50Jhyy5y9lmAP37RNvC21Hl2KKQiMs+tS47Q3DEDcWyqDALmCPg3fa3skWwBUFE9HHVo4V2k4x2FhwxhDxQtVYIATDS1DyWuiSZ3FPr7nkpVBYFFDjwHcKh5lM7m2Y+xZQJx567Reejj2vomUpxLkA3o67FI45bE50EtEJBhOt+vrsiR6ekHDoDpHiITsvRSJqzz+6QxNK6pG1GoD6gChlSDgMjqwYC12cqcx92zvYi8H0INfnVuVEB8TXByh1J/rkmskYEx+DtJnGO23vCG3rRJ/lYGisbBTaTjGyE0oU50K4YWDAFnlki1mlIKIBehQWddP20JDtTOPhRAfyiy9OEV3Vl3evQnapZKJ7Oe5Mhu8XfC8ueFHtuqnHwKttN050nUR0inMhGLW11uoRQIwLmoRDd4gUD6m4qztEO9EpzqU4NJFBEMER7UQvdeEQkBdjwdy3qoVD5+oC3m50le5bdly8s97LJRPdMAycPvZ0AMBbx98S1k7GzNhO9Cq1TvRsJjoVFiXc4BTaZAvKIiIsUil3RQ3DHqnip22ngBbki0RVlf1zPvGQievRKFBR4b+tILDXpqfHyukvhspMdFVCNu/l/n7ejzyd/5mMu3oMvEX0Qk50inMhEV1HDEOOgEvCYWFkiehEfkjAVQ/FuRBEcEQXFi114RBAVjjccWIHTDeuHJ+c6LVmbVVHWMQiMYyJW19meMdYlKIT3VkfoNTJrsoQOKHUmepExrRiDVRPKAnNRNekeCrBESboVlUBsVjw51PlRHd+OZHdtkonupe2WbvJJBCP+28zErGF9HzioQ7uW+d7opDIOXIb2WIyIEZE9yJkO/s0CF7EWp7HXF1tOy1ltu2mPoAOY4Gc6EQumHAlwoFL8QnukBHnwmNiuJQROQ6ouKs72GqAnh53Rhiv0PmIKAdIOAzOrMZZMGCgM9WJIz1HhLXDIixUu28BhwOXc4yFytx34asyymBCSUa0EXOhV1VUIRlTmzsoKhM9NZRC35DlqKM4lxKC9zJTVSJ6NGp/UfRa1DQofvOvZQumPNstJh7q4L5NJGwXvBvxUGVhURGubC9i8pgxfJb7q4pzMQxvxUXLzYlOmehELsgFrZ6Gk5+nqQ/UwQRckRMZALmgC+EsME5jgSD8kY2wSImJsCiHwqLJWBLT66cDEOvA1aWYImCL6LzFw2wOtMI4F5pQ8k822kigiM5WZKh2oQPiJpPYODBgZN3uRAnAe4mjCiErSNs8XfBu2jVNMQUsvYjEPNotlgWtg/vWKax6FZSDooMTXeUYlO1EB9REyYSlsCjFuRC5IBFdPdQH6pHRB9EoFXctRCQiZ0KJJjKIUoYJh31DfVyLRJZTYVFAkniokRM9W1CRdyZ6icW5mKZZnk70MplMEhXnwsZBXbIOEYO+EpcMOgjZKtvmIZiydvv6rOKdhejvt/O5ZYvovOMzgPzioQ7uW8CboMzTie4ULYtF6vFe7q2DkK1i/KsQ0d0UFtVhLFCcC5ELykRXD+sDEcVdKc7FHcyJ3tFR/DOcV5wrAKm4a2FoQokgguEU9ngWVCwn4RCwxcO3T7wtrA0dHbjCMtFLxImeSqcwlLE+JJSDE53VBzjRdyL7fuUNm0zSYRxkJ5MExRpRHnqJoYOQzatt9kVRdtvO5ygkpjnbBWwhOgiqjjkMTnTAm6AsIhM9nbZF1HyIKu4ZFiGbl8Cj4twTFic6xbkQuRApWlEGsTtIOFRPfb39M+/JDJpMcg+djwgiGBXRClTGLMcGT/EwW1i0DIRDQE4WdNaJrpEDl7uIXmKZ6Oy5DBjZYqylTHW8GpNrJgMAdrftFtJG1omuw4oMQeMgG2tEeeilBW8hS5Wo63we2W17yd52vt4RDtIS2383hTNVONFVi+h+BGUeY8FLoUtncU8ebmUVjmyGDi54cqKPhuJciFyQgKse6gP1xGK2kM67uCj1gXtEjQUq7kqUE0zo5ikelpsTffbY2QCAXa27hDz/YHow2z86iIf1iXoAAhy4/eocuCJF9JpETdnEcsxqnAUA2Hlip5Dnz67ISKp3orP3aSqdQmqIX4VzlSsyCIGUipDttW3eH6jdtl0K7lugeGFR5r5VHefix4nOo28Mw72o6+wXHsu9dRCydR//MguL6uREpzgXwonIgookHrpDZA4078LxpYwoAZcc0O4R1QfOz6k0FohSR4R4yDLRy6GwKGALh++0vYN0Js39+ZmoZsDQwp0q3IleInEu7LnKZRwAwOxGsRNKOjnRnZOEPHPRKc6lRBElJst2Rntp2zTVCXg6CIc8ROJiDtwwOtF558e6bbuUhGyVLngVUTLFJpMAcqKDRHRtYaIVb/ctQDEWbpERYVFXx/+5Sw02oSTKiU7joDiiJzKouCtRDggR0ZkTvUziXKbWTkU8GsdAegAHOg9wf37mvq1P1iMaiXJ/fq8wcY+ncDiUGcq+b0olzoU59ctJRGcTSrvaxIjoOmWiRyPRrJDOc1UGxbmUKKUi6nppO5Wyi0fJzmMX9Xq7KWoqM84lzE502aKuSBE9k5Hbtg4u+GJt81zazcZ9X59dMHgkOjnRe3ry76dASETXFIoSUQ/rg74++1zBC3Kiu0fUWKBx4B4ZfUDFXYlSR6QTvVziXKKRKGY0zAAA7GzlH2Ohk3AI2AUVeTrRnc9VanEuZSmii3aia1AbABBTZDcb50IiemlRzpEqgHwBX9QxO587HyriXFS7fty6sp2rE3i/J2SL6KreE0A44lx4FnJ1Fgfu7c29jQ6rMpwimptVQpwhEV1TRIlWpkkxFm6prbUcsgD/opYkortHlBOdxoF7aDUAQQSHnOh8ECkeMie6DhEWgBgnOouwGBMfg4poBbfndQuJ6HwQnomu24TSyWgjEWOBMtFLDB2c6KpE9Koq+8ur7LZ5HXM8bhXFctM2TwG/WBa0DhEWgHshO5WyHbq841xki+jJpPv3RDk60XkWck0m7QLBOq/KcBY/VhDpQiK6pogS0fv67FUwJB4WxjDE5aKTiO4e0S5o6oPi0GoAggiO0IKKZeJEB4BZDQJF9JPCoS7uWxGZ6KojLEhE5wMT0U/0nciKwTzRKRMdsFdl8IxzISd6iVJKIrrXSBVeYimgTkQ3DPdZ8OREz4/ztXO6i3m0LVtEd74nVEXJqBDR/YzBoEu7DSN8Y4FEdILBRKueHmsikRdsjDnHB5Ef0VnQJOAWhwRc9VBxV4IITm2cr3hommbZFRYFxDrRdRMOs050EcKhIvetU0Q3TZPLc2ZF9Hj5jIMx8TFoGtMEANjdtpv787NVGaXsRM9Ommky3glOqBKyS6m4p/O53ArZYRfw3RYWDYsTnfVbZaXt4g6KKhFdZdvOSYtin1lUO9F5n/Nyjf2hIbtWgeqx4CVqhzMkomtKXZ29koJnlIjzOheh3i+KKCd6x8nvACSiF4fiXNRDqwEIIji8Hbj9Q/1Im9ZSXYpz4UNWOEzqIRwKyYHuU+u+ZeMgbabRN9TH5TnL0YkOiBsL6Uw6+57TZVWGiLGg20QBwQlRQnZ3d2ERTURxT1W55F7aFingy3Tguy0sqov7VsXqhGKRNyPbFiGiq4pzSaftiZRcDA3Z75Gwi+iFnOjOQoGqx4KXqB3OkIyqKZGILeDyFA9JOPQGOdHVQwKuemg1AEEEh7eIzvLQAcuVWi4w4XB3225kzAzX59bNmZqNsEh1cHNtM7e9KuGwuqIaBqzlxrzGAnMnM7dyuSAqF729vx0mrPebLnnhIuJcdItvIjghSkQbGiq8PFxlcU8dhOywt10swkIXJ7pbYZUJ3SJWCKgQ0VXFuVRX2xEphdp2vlfDvhKl0GSJcyJBtYhOcS5ELkQIVyRaeUNEHziLu5KAWxzRRS1pLBSHjYOuLmBwkN/z0qQeUU7wFtHZ81RXVCNilM/HuWn10xCLxNA/1I9DXYe4Prduohpz32bMDLoHinxpdcnx3uMA1B2jYRjZscDiiIJSrk702Y2zAQC72vg60dlES028BvFonOtz+8U5ocQD0zS1KyRMcIK3K9spQhYSs0QW91QhWrqNsSkVF3yxOBddnOheVyeIcKKrmFhR5cqORNwdN/tfPG7deKA6zqWQEz0eVx9r4aVeBWfK51tXCBEpopN46w7WBzwjdXp67NWA1A/FodUA6qmvt38WES9FIrpetLa2YuXKlaitrUV9fT1WrVqF7iJf4C655BIYhjHs9ulPf3rYNvv27cMVV1yBqqoqTJgwAV/60pcwxJY+lwFZ4XCAz4c95sgsN/dtLBLDafWnAeDvwNVNVEvGkqiIVADgF2Ohg9uexQ/xnlAqNxFdVJwLe4/oFHOSrQ/ASUTvGezBYMZyBegyaUZwgneMRTRqCePO585FqYjJOrStQqwtFucSVie6iH5R6UTXVcAvpWMuNBZ0GQcAOdGJ3JATXT0i+oCN82hUj/OP7pATXT3RqC2k06Re6bNy5Ups27YNGzZswFNPPYUXX3wR119/fdHHrV69GocPH87evvWtb2X/l06nccUVV2BgYAAvv/wyfvSjH+GRRx7BrbfeKvJQtEKUE505NMsJUeJhtrCoJqKaYRjcxUMd3PaixgKJ6HzQMSucTRZym0w6eYyJaAJVFVVcnpPQANNUJ2aXSoFNndouJNbyLuRaLM4lbE70Uns/6lDU1M34F9VupkB8oQonuupxALif1BEAiegaQyK6ekSK6LW1dsQWkR/WB93dwMAAv+elseANNplB56PSZvv27Vi/fj0efPBBNDc346KLLsK9996LdevW4dChwtEZVVVVaGpqyt5qHbMjv/vd7/Dmm2/ipz/9KRYuXIgPfOADuOOOO3DfffdhgOfA1hjewmG55kAD5eXAFSUeqnSik4jOBzYOjvYc5fZaAsCx3mMAgAnVE7g9Z1B4Z6I7x7pBH8ZLB+dyX1Uiuijnb6G6GDqI6LLF2oEBvoVciwlyujhw2Wfrnh6r2GU+RMa56FpYVERxT+dzyXaiO11m+SZ3RLQdFic6xbkQuRAp4JJo5Q5W3FWUiE4Up77enmygflAHOx9RoePSZuPGjaivr8fixYuzf1u2bBkikQheeeWVgo999NFHMW7cOJx55plYu3Ytent7hz3vWWedhYkTJ2b/tnz5cnR2dmLbtm05ny+VSqGzs3PYLcyQcMiPrIjOMQta14zkrBOds3hITvTwU5uozQrdPCeUjvVYIvr46vHcnjMobByU0mQSIQAmpkQidgQLD7xkIosQ7zIZwPGZKm/bpVZE0s3rDfA57rA50YHCfVOq8UKyi3sC6uJckkm7voLMtgtNlugyDgAS0YnciHR+knDoDtFOdKI4kQj/yQxRqz1LGarRUB60tLRgwoTh7sNYLIbGxka0tLTkfdw//MM/4Kc//Smee+45rF27Fj/5yU9w7bXXDntep4AOIPt7vue96667UFdXl71NnTrV72FpAe8c6GwmOsW5cKFnsAepdAoAMK5qHLfnDQrr31ISD0Wtyig3ER0QMxaYE318lT4iOlsd0tbPpzCLDpNJhACcYjLPFQZuBGURIlp1tX0cqgR8XaNk2P8qK/kUcmXC4eBg7qXPujhwEwmgwqqV4kpYLRUnuhcRvaLCep14ty17DBqGGgG/0IQSGwc6iOgU50LkQoTzk+ITvEEiuh7wnlDq7bWjxWgsuIPipcLNLbfcMqrw58jbW2+95fv5r7/+eixfvhxnnXUWVq5ciR//+Md4/PHHsXv3bt/PuXbtWnR0dGRv+/fv9/1cOpAtLJrqQsYskG3okmycS5mL6GahJe4eONpzFABQGatEdUU1l+fkAXcHrgbiYW2cnOi8ECGis7GgU5wLE9FZ3YKgZOsfkBO9tBD1wVKliObGBS+yiKTuIjpv4RDQPwuaHXOhiI9Si3Nxc9CoAaQAAGUBSURBVMyixr+b4y6ltt040VVPJgFKnegx6S0SriHRSj0kousB7wkl1geGMfwzE5EfipcKNzfffDM+/vGPF9xmxowZaGpqwtGjR4f9fWhoCK2trWhqanLdXnNzMwBg165dmDlzJpqamrBp06Zh2xw5cgQA8j5vIpFAgqeTRDFM4DNhomegJ+tM90s5C4fT66cjYkTQO9iLlu4WTKqZFPg5ncKhThnJPEX0ocxQ9nlKxYmeGkphIG25BstyQqmhPJzoDZXWksTOVCcG04OoiFYEer5s8dSkPvUPCA6oFNFFLXGtqbHa1dGJLmppr4r4nHgciMWsXO2eHnsZNEMXJzpgvT6tre7ej6VSWFRVpJLz+cpNRNd9MkmhE51EdI0RIVp1nIzUJAHXHawPOjqs2h08VouRiO4d3mPBea3RSCvRGopzCTfjx4/H+PHFBZGlS5eivb0dmzdvxqJFiwAAzz77LDKZTFYYd8PWrVsBAJMmTco+7ze+8Q0cPXo0GxezYcMG1NbWYv78+R6PJpxUxioRNaJIm2l0pjoDi+jZOJcyLCwaj8YxvX463ml7Bztbd3IR0VkOtE7uW4CvA7etz47CUFk8laeI7nyOMXGOYkFImD12NgBgZ+tObs+pcyY6YE0oBd237IoMcqKXFiKFbKC8BDz2XP39lrAcyyEb9fXZS3tl57GLEmrb20tDPBQ5udHbm18YEVXcs9zc4AwVY4EKixaF4lw0RqSIXld+37l9UV9v/9zezuc5SUT3Dotz4e1EJwe0e2hlTHkwb948rFixAqtXr8amTZvw0ksvYc2aNbjmmmswefJkAMDBgwcxd+7crLN89+7duOOOO7B582bs3bsXTzzxBK677jq8973vxYIFCwAAl112GebPn4+PfvSj+Mtf/oKnn34aX/3qV3HDDTeUlNu8EIZhcBUPyznOBeAfY8Gc6DoJh4BDRO8PfvJlwmFdog6xiDofTXYcDPAT0cfExyAa4eB0CBnlkokei8Sy5zoeE0o6xBoRAii1OBeVbTufK1/bzr/zXNqr6pjDIh56EXVFTG4A+WNVRBX3LDc3uMq2qbBoUUhE1xiR8QkkorujosIen7z6gUR074hyolMfuId3H5gmnY905dFHH8XcuXNx6aWX4vLLL8dFF12E73//+9n/Dw4OYseOHejt7QUAxONx/P73v8dll12GuXPn4uabb8ZVV12FJ598MvuYaDSKp556CtFoFEuXLsW1116L6667Drfffrv041OJCAduOca5APxjLHTMgQb4OtFZhIXqwqk0Dvgxs2EmAKCluwXdA3yWNJfDWKBM9BJFByGbp2jpfD7ZgnI8bt0Kte085ghHWUlVfE5YYizcCMoi4lwSCdt9nq9t1mfO9w8PdBCyVUyiqThuNpl08nveMMI2mSQIinPRGCZadXVZhaIrgsX/ASAnuh8aG60+IBFdHeREVw9vEb27214BSucjvWhsbMRjjz2W9//Tp08fVsxx6tSpeOGFF4o+77Rp0/Cb3/yGyz6GFSFO9DKMcwH4O3CZ+3ZClV7CIXPKMgE8CMd7j1vPqVg4JBGdHw2VDRhbORYn+k5gd+tunN10dqDn6x3sRe+g9cVZx1UZe9r3oK2/rfjGRWDjiZzoJUYpimiqXfAnThQX0UvlmPM50U3TFg91ENG9xLnwFNFZoduOjuIieimIyeXadlWVdZ9LRNdxMomc6IST+no7r5mXcEWZ6N7hLR6SiO4d3n1Ak0neYRMZvPsgFtPjOkwQMuAqovdTnAtATnQv6BJhwcYBew8HgU0mlauIDvDNRWd56PFoHDVxvZwGIsaCytoAhABEucFVRkkUE4pMU91x6yCi8zxmJqKPdKIPDNg/6+TAVVXotlDbpTyRVS4u+EIiOjnRAZCIrjXRqJ3JTeKhOkQJuCSiu4e3E53GgXfYOBDRB1TclSgXyIHLD6eI7lwZ4RftM9E5xrmodqKz1RM0DvjAxsLOE8FFdOdkkqHZxbmhsgFAaY0FgjM6iLqy2+7vt5d2yhbwRAm1rF1W1DQXogqLAqNFdCYcAno4f1RlortpW7SYPDAwfFJDZts6OtFFFHJlInpYYo0KvScEQSK65vAUcNNpeyyQeOge1gdtwVePAiAnuh/Iia4e1gcdHfk/z3qB+oAoRyjOhR+nNZwGAwa6BrqyUSxBCIMTPehkgS5OdLZ6or2/PfBzkYjOtz6AjkVFGY1JPhNK6Uw6+95TPRYIzqgU0VU7fwFxTnQ3megi2gXkirX54lyYcGgYfHO+/aIqE93ZtionOlC8qGkpiehuV4PwbDtsmeiA9EgXEtE1h6d42On4zk4CrnsaLOMLxbkoRFSUCAm47mHjAADa24M/H/UBUY7wEtEzZgZdKesDY7nGuSRjSZxadyoAPg7cbCa6ZiI6c8oOZgYDF47UJQe6PlkPwJoICjoxwMZSuY4DQEyci24rMgB+qzLa+9thwhz2nESJUI5OdPb36mq+xT29tM37mBMJuxiczLbziYdO960OK3SKibqmKU5EV+VEdxYqVeWCV1lYWGYh17BkoldUWOcKQHqkC4nomsNTRGeiVTKpxyRqWOAdY0EiundERokQ7ojF7Pcsz0k96gOinGAietdAMMdE90B3VgQqawcup1x00zS1daJXxiqRiFpfEoKKh1knuuIICyaiD2WGskUs/UJOdGB2I0cRXdPJJICfiM7GQU28BhXRisD7RWhEKYvosoVD53PKFtFVtZ1PPNTJfQsUF3VlRPyoeD+qjpLRMc5F5DhIpawoCye6jQVFxUVJRNccESI6iVbeoMKi6mF90NtrT4AGgcaCP+h8RBDB4OVEZwUZKyIVSMY0cIMogpeI3t7fjqGMlVOlW4yFYRjcxUPVTvSqiirEIjEAwSNdSES3negt3S3ZFSp+ydYG0GwcAPxE9OO9xwGon0wiBCA6o9tNcU/ZbZeakK2y7Xwiuk7uW8C9sArYx8QLle8JVWPBOZGVa/WcjPGvQkQHwj8WBEEiuuaIiHMh0cobPItamiaJ6H6oq7MK7QIk4KpEhIhO44AoJ3iJ6NkIi2SddsX/ZJIV0duCiehMOKxN1CIRSwTeL95wE9E1KaZoGAa3XHRWG6CcRfT6ZH1W9A7qRtc6E/3kOGjrD1akiEXW6Oi2JwKiSkTr77cdm6riXEpFyHbbtoi4krA40YutTmB/r6qyv0Dzopyd6ENDuQtYplJ2wbBSmERzxhblGwu6iOil5kRvbW3FypUrUVtbi/r6eqxatQrdRWYILrnkEhiGMez26U9/etg2+/btwxVXXIGqqipMmDABX/rSlzDEo8qdpvAUcEk49AfPPnAWGSfx0D2GwTebnsaCP8iJThDB4OZEJ+EQAD8nus4RFoAtejMnuV90caIDw3PRg0BOdItsLnrA+gA6C8wNldYHwaCTSbpGNxEcEC2ip9O5l8Q6BRyWq827bdnFPZ3PKVPIZpATPT/FxORS65eRbcsW0Z1jOlfbIov7qpg4MIziY0G3CaVSEdFXrlyJbdu2YcOGDXjqqafw4osv4vrrry/6uNWrV+Pw4cPZ27e+9a3s/9LpNK644goMDAzg5Zdfxo9+9CM88sgjuPXWW0UdhnLI+akenkUtmQvdMMRc10oZmlBSD8+xQH1AlCM1cevDHq84l3IupgjYIvrOEzsDFajUXVTj4UTPmJlsjIUORSOZiE5xLnzglYuejXPR4D0yEl4rMnR22xMBEe1EdbaRq10VxT1FRdg4n7NcXMdhcaKrjPjR2YkuaizEYvYESq7XnLVbWWltyxO3Y5C3sBS2sVAKcS7bt2/H+vXr8eCDD6K5uRkXXXQR7r33Xqxbtw6HDh0q+Niqqio0NTVlb7UOxfd3v/sd3nzzTfz0pz/FwoUL8YEPfAB33HEH7rvvPgzkWlpRAlCci3p4iresD2pq+H/GKnXIBa0engVeqQ+IckREnEs5M7NhJgDLzcwEYj/onAMNAI3J4OKhbrnv7L1LIjofeIvoOk4oOUX0jJnx/Tw6HyMREFECXiRiu1ELiWjlFqlSam2TE9192ypWJ7htW7aAL+OY8/V1T491z/uYwzIWSsmJvnHjRtTX12Px4sXZvy1btgyRSASvvPJKwcc++uijGDduHM4880ysXbsWvY6O27hxI8466yxMnDgx+7fly5ejs7MT27Zty/ucqVQKnZ2dw25hgYRD9bA+aG8fXaDYK5SH7h9yoquHJvUIIhhM6AsaYUFxLhaVFZU4te5UAMHEQ50jLAA+DlwmHNYn67XIfScnOl9OH3s6gGBxLqZp4nD3YQDApDGTuOwXTxqSVpxLxswEKqCqe3yTaO677z5Mnz4dyWQSzc3N2LRpU95tf/CDH+A973kPGhoa0NDQgGXLlhXcXimZjDhByfmchZzopSIm6962qEKObKKEvY8Yurlv3Waii4xzkS3qAnpMHuRqmx2zChFd1DEzEV33sVBKTvSWlhZMmDD8g0ksFkNjYyNaWlryPu4f/uEf8NOf/hTPPfcc1q5di5/85Ce49tprhz2vU0AHkP290PPeddddqKury96mTp3q57CUQHEu6mF9YJpAW7BaRiSiB4DXWBgasq8HJOB6gyb1CCIY2Rzo/oAiOsW5ZGHi4dsn3vb9HEd6jgDQV1TjKaLrcoz1iXoAwcdCdlVGmY8FlokeZBy09bdhIG2t7J04ZmKRreVTWVGJypj1xZ3HWNBhRYZsfvazn+Gmm27Cbbfdhi1btuDss8/G8uXLcfTo0ZzbP//88/jIRz6C5557Dhs3bsTUqVNx2WWX4eDBg5L33EEmAwwOjv67U0SRnQUtK8Iik2MFRjmK6KIKuYbFfasq1sRL27IF5cFBq8AnwL8uAVB48kDGMTsL6+Vqm/cxs+ejwqI58SSi33LLLaMKf468vfXWW7535vrrr8fy5ctx1llnYeXKlfjxj3+Mxx9/HLt37/b9nACwdu1adHR0ZG/79+8P9HwyIdFKPRUVtugdtB9IRPcPrzxu5zmWxoI36HxEEMFwFlMMEkdAwqHN6Y3BRfRDXVbUoI7uW4BPYVHdRHRecS5MhK9JCBALQgSrD3Ci7wTa+vw5Pg53WS70hmQDkjFNviCPgBUXbev372rRbSzI5J577sHq1avxiU98AvPnz8cDDzyAqqoqPPzwwzm3f/TRR/HZz34WCxcuxNy5c/Hggw8ik8ngmWeekbznJ/nSl4BoFLjtttH/Yx/wo1ExAk+hKAkZYjIwWtCS1bZuhUWdQiZP8bBYDrQuwiF7vVOp3BNKOhSblS1kOx3TsgV8kcfsPBaZAj4VFi2Ip+T7m2++GR//+McLbjNjxgw0NTWNmtEeGhpCa2srmpqaXLfX3NwMANi1axdmzpyJpqamUUvIjhyx3EOFnjeRSCCRUL901Q9MOOzosCafgtQqoPgE/4wda71+QaNESET3D688bibeJpNAPB7sucoNWhlDEMFgInrGzKB7oNt3BAXFudjwcKIzEX1yzWQu+8QbHk70I916ue15xLn0D/UjlbacZyzqo1wZEx+DSWMm4XD3Yexs3YklU5Z4fo5slEuNnpNJgDUWDnUdCjQWdI9vEsXAwAA2b96MtWvXZv8WiUSwbNkybNy40dVz9Pb2YnBwEI3sA6Fs2Pf5YmKSYfBvW5UTtarKOh4WYTKyDZV5zKqy4J2FHKNRfm2GRTgcKaw2jLj+qYxzUR2pEouJ+YKvKs4lHreOaWjIaru+Xk7bVFi0IJ4k2fHjx2P8+OJL35YuXYr29nZs3rwZixYtAgA8++yzyGQyWWHcDVu3bgUATJo0Kfu83/jGN3D06NFsXMyGDRtQW1uL+fPnezmU0OAcJ21tgIuXPy/k/PTP2LHAnj38BFwRnzVKHV5OdBoH/iEnOkEEIxlLIh6NYyA9gPb+9sAierkXFgX4iOhMPCxlET3rvq3SQzh0rsrwCxPgDRhl70QHrEiXw92HsfOETxG9S988dAYbCyd6/X0gz5iZbCb6+OryinM5fvw40ul0zmhUtyvJv/KVr2Dy5MlYtmxZ3m1SqRRSLFYB4FuLjDk9CwlZor5kMbFmZEawc39EtG0YVttdXYXbVimii3TgyjzmsAiHFRXWhFIqJV9EL+ZEl5EPXmhipbpazCSam7ZFHLNhWOeVtjY1TvSR40+3aKM1a4BPflK6K09IJvq8efOwYsUKrF69Gps2bcJLL72ENWvW4JprrsHkydYXlIMHD2Lu3LlZZ/nu3btxxx13YPPmzdi7dy+eeOIJXHfddXjve9+LBQsWAAAuu+wyzJ8/Hx/96Efxl7/8BU8//TS++tWv4oYbbgit07wYsZgtMvESD8n56R3eLuiRk4hEcXj3AYm33uElopsm9QNRnhiGwcWBS3EuNiwLemfrTl8RORkzY4uHmjpwSzETnb13g4wD9ti6ZB0ihpCvNKFidqM9FvwQBif6uKpxAIDjvcd9Pb69vx1DGStXthwz0YPwzW9+E+vWrcPjjz+OZAEBRWgtMjeRCiKELLdtixCTi7UtQ7TMJWSLbtvNhEm5iuiAu/ejyBUCujnRy3X8i2o711hIp+34IF3GQm0tMGGCdFFf2CfORx99FHPnzsWll16Kyy+/HBdddBG+//3vZ/8/ODiIHTt2oPdkx8Tjcfz+97/HZZddhrlz5+Lmm2/GVVddhSeffDL7mGg0iqeeegrRaBRLly7Ftddei+uuuw633367qMPQAl7CFYlW/uHlgmaFSUdOGBPF4T0OaDLJO6wP2tpy1zZyS1+fXRuFzkdEucFDRGc50BTnAkyvn45YJIb+oX4c6Dzg+fEnek9gMGN9KWga4z5yUCZjK09moveegGmavp7jaK9eIjqPccAey56r3GGrMvyK6LrXBgBs4Zu5yb3ColzqEnVIxErTgJWPcePGIRqNZqNQGUeOHCkat3r33Xfjm9/8Jn73u99lzW35EFqLzI07udSE7GJty3CD9/bahTwZAwO2oCZbwJcpHAL6uW8Bdxn9soVs05TzflQxBt3ksasY/6LazlVYlE0mAXqNBQUESNguTGNjIx577LG8/58+ffqwLwJTp07FCy+8UPR5p02bht/85jdc9jEsNDZaUSK8ilqSaOUdJqIHdUG3t1v35ET3Dq8+oMkk/zARnTnJ/U4GsXNRJCLu8wZB6ArLbg4kolOcS5ZYJIaZDTOx48QOvH3ibZxad6qnxzP37fiq8YhH9SyUwZzog5lBdA10+Zo80c2JTiI6f5gT3W+0UdaJHgYRvcefiM7GQblFuQCWYW3RokV45plncOWVVwJAtkjomjVr8j7uW9/6Fr7xjW/g6aefxuLFi4u2I7QWmUoRrZAzWrQLVlXbTiG0t3e4s1lUcU+GCtcxE9EHBoYXowurE12kiD4wYN2c+eN9fdaXRFFtq6pL4HxelW3LjJLJNaHEJpMAvcaCAmjtYwggJ7p6SERXD40D9cTj9jU6yFhwrgYQEVtHEDrDBL+2vjbfz0FxLsMJkouedd9qHGFRHa9GVYX1hSaoeKiLiM4mgHhkopOIbpGNNjqx09eKBd1jjQD7/ctWVniFOdh1GQeyuemmm/CDH/wAP/rRj7B9+3Z85jOfQU9PDz7xiU8AAK677rphhUf/7d/+DV/72tfw8MMPY/r06WhpaUFLSwu6JRdxy6KDkKXSBZ+rbZGTB5WV9gf1ka85azcetzK6eaNiwoQJh0BuB65O7ltVgvLIoqZOnO9P52vJu+1yiVRR2XauTHQ2DuJxywlXxpT30YcEHuKhadruT4qx8A6vPG4S0f3DJjL6+0evsvMCiejB4HE+okgdopyhOBf+8BDRdS0qysiKhz3+xEP2uIljJhbZUg7kROfPzIaZAKyJCT+Z4aFwolfzcaKXq4h+9dVX4+6778att96KhQsXYuvWrVi/fn222Oi+fftw+PDh7Pb3338/BgYG8Hd/93eYNGlS9nb33XerOQDdhaxSc8GyoqbAaAG/FN33yaQ9aZDLgauT+1ZVJjoragqMdkY7x6AIkbWQI1tWpIrKtlXXB9Ax1kgRwuJcCH7wcEH39NhRZiQeeodXJjqJ6P4ZM8ZaVTc0ZPWD3wluEtGD0dgI7NvHR0SnPiDKES4iOsW5DCNIFnRWRB+jt4g+vmo89rbv9ZUFPZAeyL7fdBEP2TjoH+pHaijlK5+aRPThVFZUYmrtVOzv3I+drTs9R5aEwYkeNBM9G+dSxkVF16xZkze+5fnnnx/2+969e8XvkBdURqqUYyY6a7ura3TbKt33oto2DOsLZk9Pbie6jiK67Ex0wBLnUyl17wndxr/otlWsOiiUia7TOFAEOdFDAA/nJ3OhR6NiVteUOhTnoh7D4DOZQQJuMHg60akPiHIkqIieGkphID0AgOJcGEGc6GEQDoFgTnTm2o1FYtoIzjVx2yHnN9IlK6In6jnsUWmQnVA64W1CqTPViZ5BS6zSeVVGUCc6e5wuk0mER1TFmgB6ZqJnMrbIJbttmRMHI+OpRLady4GrY5yLjoKyDmKyKAFfZdsq41x0HweKIBE9BPCOT6AMYu+QiK4HPGJ1SMANBonoBBGMrIieavf1eCYcGjAwJk6VeQFbONzTtic7weCWQ92lH+fidN9GDD0++kcj0Wwckd8JJXKij8ZvcdF9HfsAWIWPdT6vMAf5ib4TyJgZz49nWeokooeUQsJqKQpZxdp2ClyqBFPRxzw0ZBWwzNW2LBFdxzgXHYpsjnTBy5pYYUVNVbSt00qUdNp+b4qKc3FOWOo4DhShxydpoiA8RKu2k/XLGhqC7085wkNEN01bRKd+8Ac50dVDIjpBBCOoE72t37qg1yXrEI1EOe1VuJk0ZhKqK6qRNtPY07bH02P3d+wHAEypmSJi17jBRD8/Dlxdc6CDjgUS0UfDiou+3epPRD+17lTu+8STcVXjAAAZM4PWPu8fRCjOJeQwoSidtuIknOhQWFR226xdwxAnbKnORHe2NfJ3EQJ+IQeuTuJhIVGXidsiMtGdzyvbie7sb9nvRx2c//kmLUS0HZZxoAgS0UMAiejqYX3Q22tPwnmlu9vOpScnuj/Iia4eEtEJIhgNSetC7FtE77Mu6CQc2hiGkXWj7zixw9Nj3+14FwAwrX4a9/3iCRP9mJPWC7qK6CyOiER0fswbNw8AsP3Ydk+PY5NJuovoFdGK7DnUz6qMlu4WAEDTmCau+0VIopCwWo5OVNZudbW4pebF2hZ1zLGYXcBypGAqO85Fx4KK+YTVTEZdkU3RqxPicetWqG0VhUVVrURhv0ej9ljhRVjGgSJIRA8BJKKrp67OOj8B/vuBudArKmgCzy8k4KqHzkcEEQxeTnQmJBEWc8fNBeBNPOwd7MXx3uMAgGl1eovoQeJcjvQcGfYcusDGQke/v0x0lqVOIrrN/PHzAVhxLoPpQdePC4sTHQiWi84KCeteA4HIQzRqCzg6FjVUJaKJEg6B/Jnooo8ZUHPcYXHg5ntt+vrsqCPZ2eQy3o+q44VUTKIVe71FTKJRYdGCkIgeAki0Uo9hBHdBO/PQKZfeHzxidShSJxg8+oDOR0Q5wwQ/5ij3CntcQyUNICdnjD8DALDt2DbXj2HC4Zj4GO2F2CBxLsx9O7F6Itd9CgrFufBnat1UVFdUYzAziN1tu10/bl+nNRam1k4VtWvcYKsyjvV6Gwu9g73oTHUCsCKgiJCiSlDWsbBoqQrZbtsu5ziXYsKqYdjHwhtVqxOA/MctU8jOV49B1UqUci6wqwgS0UMAE63a2+04EK+QaBWcoOIhFRUNDo8JJfZYGgv+oD4giGDwEg7JiT4c5sD1IqK/234yyqVuGgzNZ7eZ+9aPE525b6fU6pX7TiI6fyJGBPPGW5Eubx570/XjysGJfrjrMACgqqIqW9SWCCH5MrplRViMbHdgwCp+KaNtFaKlqtcbyD95ILJt1maugoo6iYf53hMsbqS6GogIkvqKxbmonFgRPQZHFro1zeGxSiLbVrEigwqL5oRE9BDAhCZnYUqvkIgeHJ5OdMIfQScyUil7EpXGgj94roxhz0UQ5QQT/DpTnciYGc+PpziX3JwxwXKibz+23fXryoRD3fPQAYcTvfcYzJEuqCIwEX1yzWTu+xUEykQXA5tQ8iKihyUTHfDvRM9GuYyZpP2kGVEA1XEOg4PDRTTnfohuW3YxRedzq3TByzzusDjRVQrZbuJFRKHq/ZivHoOM+BxakaEdJKKHgIoK+1zlV7giET04TMANmolOfeCfoAIuGweGQZnofqF4KYIIBhP8TJjZaAEvUJxLbmY0zEAimkDfUB/2tu919ZhsUVHN89ABWzgcygx5Fp0Pdh0EoJ+Ins1ET3nPRO8f6kf/UP+w5yEs5o/zJqKnM2kc6DwAIBwiOisKymKK3HK423Ki6zYOCI+ojnMZ2Tb7OZGwimGKQKWQXSxKptQE/JHioWnq6cDVIWZHpRN9ZNsyCt3mqsfg/Fl0fI7MY2bHkkrZURg6rshQBInoISGocEUCbnAozkU9QScy2OPq68WtcCt1nOeijHcTLQAS0YnyJhFLoDJmfRHz48AlJ3puYpEY5oybAwDYdtRdpAsT0cMgHCZiiWwEBSsU6gbTNO04l5rSiXNhxUgNGKhJ1HDcq/Dj1Yl+pOcIBjODiBiRUBTcZHnmTBR3CxUVLRFUiYcVFUA8PrptHYp7qhCyZUbJqMyCTqXs/+kkHhZzg9cIvC6qyiV3Prcukwes3aoq8fE5Mo/ZOUHFHOjkRM9CMlJI4OXAJdHKP0FFdNYHJKL7J2ikDo2D4LDXLpMZPSHuBtOkfiCIIOJhVkQnJ/ooWHFRt+JhNs4lBE50wHbgHul2L6J3pjrRO2iJAbqJh3VJ/3Eu7DF1yTpEDPo644SJ6G8dfwvpTPFiSmzlxim1pyAWEeSk5Qh7HzNR3C0sE52KioYcFTnZjFxiVqmKySPbzhepUmou+JEiOhMOAb3Ew2KZ6KXqRC82eVCK419FfI5zwogdIxUWzUKfOkMCLxGdBFz/kBNdPU4nusdIWACUxc2Dykr7M6Sf81Ffnx0lSSI6Ua4EEtH7yImeDyaiuy0umi0sGoJMdMCOofAiHrJt65P1qKoQtNTYJ0HGAeWh52d6/XQkY0mk0im80/ZO0e13ntgJAJjVOEv0rnEh60Tv8uhE79azNgDhkVyi7sCA/eFSdka3TPFOhZCtg/PX2bboQo75RPRo1FqNoAs6RKrolIkuurgnkFvMlvl69/fbRYxFt20Yo8eCjrFGiiARPSSQA1c9vCJ1SET3D+uDgYHRnyPdwPqOxkEwgowF9phoVOznDYLQGSb8MUHcC+REzw9z4LoR0fsG+7JO9JkNM4XuFy+CiOg6CoeNldbFpLXP+8WERPT8RCPR7Fh47chrRbff2WqJ6LMbZwvdL14wJ3pLd4unIrvkRC8RCrlBAfkCnkzhMJWyCpuObFtFlIwqF7zoQo75hEPd3LfFCt3KcEbrIuA7J9Fkty1z/APDx4LoMZhvQolEdBLRw0LQLGgS0YNDTnT1VFVZdXsAf2OBxgEf2Fg4ftz7Y519YBj89okgwgQ50cVw1sSzAFhxLkOZoYLb7m7bDRMm6hJ1mFA9QcbuBWbyGBLRGSSiF+acpnMAAK+2vFp027CJ6CzWaDAziBN97j+Uswx13WKNCI8UEtHjcTu3XFbbMoVDQK6I5nxuXVzHogs5hkU4dPa58zVhwrbITPR8KyNkvh+dAr7KSTQZxxyP20WLZZ572Gup+4SSAkhEDwlBnOiDg/b4JvHQPySiq8cwgo0FinPhw/jx1v2xY94fS31AEA4nej850Xkyo2EGauI16B/qx1vH3yq47Y7jOwAAc8bNgRGSGT0mhB/sOuj6MTqL6GMrrQ9WJ/pOeHIUAySiF+PcSecCcCmin4xzmT02HCJ6PBrHuKpxALxFumQLi5ITPdyocoM6n79cRDTnc6sQTHO93qILObI22fHpGmERi9liZrkXumU/O4v/imw7V2FRkcdsGIXbFnXeYxNKIzPRdRsLCiARPSTwEK0AoK6Oz/6UI7xEdJrICAaPKBHqg2DwOB9RHxDlDBMPvTpwB9OD6B6wPjSTE300ESOCs5vOBgC8eriwePj2ibcBAKePPV34fvEiUJzLGP1EdOZEH8oMZd/XbiERvTDMib7l8JaC25mmGTonOmAL4W7HQleqK/uemVo3VdRuETIoJKyKzgkslIkuWsBXHSWhm2AqO8JCR/etKlFXZUZ/rlxyGRM6zudXMYGnwgUfllUZCiARPSTwEK3q6qwcYsIfrA9OnAAyGe+PJyc6H4JMZpCAywcS0QkiGEw8PNHr7UTmjH+pS9KseC7cxljsOHHSiT52jvB94sWU2ikAvInoB7oOANDTiV5VUYV41HKNeZ1QYisy6hP1vHerJFgwcQEMGGjpbkFLd0ve7Y70HEH3QDciRgQzGmZI3MNgsEgWFtFSjP2d+wFYky61iVph+0VIQFWkQr62VQr45RLnQjnQuVH1ftTViS56DKoU8FUUNQ1LfQAFkIgeEki0Us84a+Uo0unh7n63kIjOhyBOdIoS4QOdj0qb1tZWrFy5ErW1taivr8eqVavQPfKDsoO9e/fCMIyct1/84hfZ7XL9f926dTIOSTvGVtkxFl5gwmFNvAaxSIz7fpUCbkX0sDvR3cafvNv+LgDg1LpThe2XXwzDGBbp4gU2AcXGEjGc6ng15o6bC6DwqgwW5XJq3alIxBJS9o0HzInuNs6FFRHWcRwQHtHVDV6qAj57TVMpYOhkrZGBAfvnUivkmE841FFEzyWsysxEHxqyC3pmMvZrVqoiuq5tyxoLOk8oSYZE9JBAopV6KipsAdxrP6TTJKLzIkiRXYpz4UOQ8xH1gf6sXLkS27Ztw4YNG/DUU0/hxRdfxPXXX593+6lTp+Lw4cPDbl//+tcxZswYfOADHxi27Q9/+MNh21155ZWCj0ZP/AqH2aKilIeel2wW9OFXCwrNYXSiM+Gwb6gPHakOV495t8MS0afVTxO2X0HwW1y0tb912OOJ0ZwzqfiEEhsHYYpyAewJJbdOdBLRSwiVQpaqTHTn86uKcwHsY3Xug8i2VUT3hDHORdXEirM99nqJbrtQYdFSjVTK17boc8/I+gA6jwXJkIgeEphodfw44LH2EonoHGH9cPSot8d1dNj9Ri7oYPAoLEpjIRg0qVe6bN++HevXr8eDDz6I5uZmXHTRRbj33nuxbt06HDqUO0IiGo2iqalp2O3xxx/Hhz/8YYwZ8cGuvr5+2HbJMv0glnWie4xzyRYVpTz0vMwfPx+JaAIdqY6s23wkR3uOZkXbWY2zZO5eICorKrN97ybSpWegB8d7jwMAptXpKaKzseBZRO8jEb0YbnLRXzvyGgAr/iVMeM1Ez4rotSSihx5d41zKqagpu4/HLZeZKCgHujCqRHRnAc+REyuGIVZk1cENnkvAL0UXfJhWZUiGRPSQwESroSHb0ewWEq34MWGCde9VPGSCb02N2KLR5UAQJzrFufCBRRsdP+79sdQHerNx40bU19dj8eLF2b8tW7YMkUgEr7zyiqvn2Lx5M7Zu3YpVq1aN+t8NN9yAcePGYcmSJXj44YcLOoVTqRQ6OzuH3UoFv4VFyYlenIpoBZZMWQIAeGn/Szm32dqyFYDlvq2OCxY+OOOluCjLga6J12hbgNNvfYBsnEslxbnkY/Fk6zz+pwN/ynuu/cuRvwAIn4jOioMycbwY5EQvIXRwg+aKklFZ1FRk24Yx+jWXLRzKPOYw5UCrKiwKjJ7UcbZrGOLaVZENztBNwKc4F2WQiB4SEgn7nOFVwCURnR9+HbhMRB9L3/UC49eJbpoUJcILcqKXLi0tLZjAZgtPEovF0NjYiJaW/MXpnDz00EOYN28eLrjggmF/v/322/Hzn/8cGzZswFVXXYXPfvazuPfee/M+z1133YW6urrsberUqd4PSFOCZqKTE70wF069EADw0r7CIvrCpoWS9ogfTETf37G/6LYsD31a/TQYIr/UBqAx6TPOhZzoRVkyZQkqIhU42HUQe9v3jvq/aZpZJ/rZE8+WvHfBYCsrWFxRMUhELyFURHwwdBDRdHDBl/IxM+Gwr8/K+dZZOMz1+sjIRHe2PdKJrtINXg5xLjInTMI0oSQZEtFDhF/hikQrfviNcyERnR9+nei9vcDgoPUzjYVgsHHQ1ma/pm6h85EabrnllrzFP9ntrbfeCtxOX18fHnvssZwu9K997Wu48MILcc455+ArX/kKvvzlL+Pb3/523udau3YtOjo6srf9+4uLhmGBCX+9g73oH+p3/bisE51E9IJceOpJET2PE51lRLO4izAxvX46AHfiYTYPXdMoF8D/hBLbngqL5qeqoirrRn/x3RdH/X9fxz6097cjFolli5CGBZbxf7TnKPoG+4puTyJ6CaHSDV4oUkW2gJdO2wKvbEe4CvGeraaRlQMNWK9vGER0lSsjZK9OYP0zOGh/CVW5GkTlqgxZY4Gc6KMgET1EkIiunqBOdIqwCI5fJzobB7GY+OtcqdPYaK/U89sPdD6Sy80334zt27cXvM2YMQNNTU04OmKWcGhoCK2trWhqairazi9/+Uv09vbiuuuuK7ptc3MzDhw4gFQqlfP/iUQCtbW1w26lQl2iDlEjCsBbjEXWiU5xLgW5YKq1CmLHiR041jP6gr350GYAduHFMMFE9D3te4pum3Wiayyi+yks2jfYl518Iid6Yd5z6nsAAH/Y94dR//vTgT8BsFZkJGIJqfsVlIZkA2riltOy2IRSOpPGgc4DAEhELwmcYlImY/8MqHWiyhbwZRVydD6/qjgX07QFPNGvt1Mg7O3V232rclVGvtUJssYgIN8Fr8MqGHbMg4MA+/4k2one0zN8DOo4FiRDInqI8OuCJtGKH0Ez0cmJHhy/TnTnONB0VXtoiEbtfvA6FihSRw3jx4/H3LlzC97i8TiWLl2K9vZ2bN68OfvYZ599FplMBs3NzUXbeeihh/ChD30I49kFqwBbt25FQ0MDEolwiTc8MAzDzoL24MBt728HQE70YjRWNuKM8WcAAJ7b+9yw/7V0t2Bn604YMNA8pfh7WjdOqz8NAHLGc4wk60Sv11dE91MfgI2ZWCSWFVKJ3Lx32nsBAC+8+8Ko/728/2UAwAWnXDDqf7pjGEb2fc0mi/JxuPswBjODiBpRTKqZJGP3CJE4BSMmJJd6Lrnz+UeKlqILORZqW7RgykQ8QJ4LPhKxX8/eXr3dtyPfj5mM/XOpxrnkKnSrwySa7Lad5yAZmehDQ/akpY5jQTIkoocIvwIuFfLjh18nOhMOSUQPDnsft7baK/vcQOItX/yMBdOkST3dmTdvHlasWIHVq1dj06ZNeOmll7BmzRpcc801mDzZymI+ePAg5s6di02bNg177K5du/Diiy/ik5/85KjnffLJJ/Hggw/ijTfewK5du3D//ffjzjvvxI033ijluHQkG2PhwYlOERbuWT5zOQDgt7t+O+zvf3jXcuQumLgglI7+rBO9zYUTPQRxLn4mk5x56LpmvevCRadehFgkhl2tu7Crddew/7G4I7ZyI2y4zUVnxz29fjpikZjw/SIEU1lpu2F0yOhWFecgq5AjoC4TPRq1hTyZLnineMic6DoKhyP7xSmsqiwsKhpVAj47ZhVxLvn6OhazJhZE4HSi9zli03QcC5IhET1E+BVwjx+37seN47s/5QhloquHvYZDQ0BHh/vH0WQSX/ycj3p6rH4DSETXmUcffRRz587FpZdeissvvxwXXXQRvv/972f/Pzg4iB07dqDXuZQYwMMPP4xTTjkFl1122ajnrKiowH333YelS5di4cKF+N73vod77rkHt912m/Dj0RU/DtzjvdYFfVwVXdCLcfnsywEAv935W6Qz6ezfn9/7PADboRs2TmuwnOgHuw5iID1QcNvdrbuHPUZH/MS5sIkninIpTl2yDhdPuxgA8OSOJ7N/P9pzFFsObwFgCe1hJCuiF3Gi7zyxEwAwe+xs4ftESMAw8gtK5ehEVSFaymxbhVjrFNF1jrDIJyYbhnihs9zeE87n16GwsIxJNGeR3X5H/aYyXEE8EhLRQ4RfEZ1tTyJ6cCjORT3JpL1CzctkBjmg+eLnfMQm9JJJ8d81CP80NjbiscceQ1dXFzo6OvDwww9jjOPD4fTp02GaJi655JJhj7vzzjuxb98+RCKjP1qsWLECr776Krq6utDd3Y2tW7fiU5/6VM5tywU/DlwS0d3znmnvQX2yHkd6jmSjLDJmBr/e8WsAwGUzR0/2hIGJ1RORjCWRMTPY35G/2G5nqhOHuw8DAE4fe7qs3fMMW1XhRURn27KJKKIwHzz9gwCAn7/58+zffrPzNzBh4txJ52JK7RRVuxYItipjb8fegtvtbD0pojeSiF4yFBKURDJSvDNNPUQ00aiKc1HVdi4RXUf3bSExWdXqBJXvCVljMJWyXWGyJ9FkFvdl73lngd1kknJxQSJ6qPAjWmUytoBLInpwWB8cP27HQrmBRHS+TJxo3R854v4xTMClPuBDEBF93Di6/hKEnzgXEtHdE4/GcfUZVwMAfrj1hwCsDOhDXYdQm6jFX834K5W75xvDMGzxsEAuOnPfTqiegPpkvfgd84nTiW66zGhzxrkQxfnwGR9G1IjiTwf+hG1HtwEAfrbtZwBsgT2MuM1EJxG9BFEV5+AU70zTcmeyL4SlWsgR0EPAl+l4Dkucy8h+6eqy7kXnoTvblj0GgdGxKrJWojifnxU2ZqtyS7G4L3vP674iQwEkoocIP6JVRweQPrmKmUT04LDXMJ22nc1uIBGdL01N1r0XEZ2NGxf1DgkXOCeU3EKrYgjChrlo3TrR05l0VjwkEd0dq85ZBQD479f/GztP7MS3X/42AOBv5/0tErHwLkfN5qK3589F33FiBwBgztg5MnbJN2wcDGWG0DXQ5eoxVBvAG5NqJuGDcyyx/M4/3ok3j72J9bvWw4CBaxdcq3jv/MOK7O5u211wO4pzKUHyCUqy3KBMQHdmIzuLYIpsW4f4DBVRMhTnMpqRYrIOqxNKue1EwsrpB+yccDb5X4oZ9LniXHScTFIAieghIojzs7ZWXM2BciIeB+rqrJ+99AMT0SmPmw/Mid7S4v4xJKLzJcj5iPqAILyL6O397ciYmWGPJQpz3pTzcPnsy5E203j/j9+PJ3Y8gYgRwVcu/IrqXQsEEw/faXsn7zY7jlsius5RLgBQWVGJZMwSJ9yuysg60ZP0ocotX33PV2HAwGOvP4bLfmJFGV0590rMapyleM/8M2ecNUHU0t2Cjv7cRXIyZiYrspMTvYRQJaI5hfLubrvdykpbXBOFDqKlzCgJhgoHvlOc1jnOReV7Ip8bvJQLi46sx+DMRpedQa8qzkXHcaAAEtFDhFO0crnilYqKCsBPLnrryahPcqLzIUicC40FPgSNcyGIcsdrnAuLcqlN1KIiWiFsv0qN/1j+H2hINuBA5wEAwBfP/yLmjpureK+Cwdzl249vz7vN261vD9tWZ7wWF6XCot5ZNHkRbl56MwCrKG1DsgHf+qtvKd6rYNQmajG5ZjKA/GPhQOcB9A/1IxaJZeNfiBJAlXgYjdoiklNEK9dcchXHLTPGQncHro5OdBUTK7JWogDDxWxnu6JrPKkYg844FzYOdFyRoYCY6h0g3MNEq4EBK/Kqtrb4Y0i04s/48cDOne7Fw1TKPseSiM4HPyI6OdH5wiaTqA8Iwh/MTe5WOKQ8dH/MHjsbWz61BT/5y08wq3EWrj7zatW7FJgzJpwBANl861wwJzpz6+rM2MqxONR1yL2I3kciuh++9VffwuLJi7HjxA5cu+BazGiYoXqXAjNv3Dwc6jqE7ce24/xTzh/1/7dPWJNJMxpmIBahr70lg+p4kb4+q+1yjVQpdddxWOJcVGai6yDgq8xj7+62V5/IHoOmKbc2ADnRR0GfJkJEVZU1bnt6LOGKRHQ1MAHw6FF327Mol2jUjoIhgkEiunpYLr2XSB06HxGEDRMA3ca5kIjun+n10/G1i7+meje4ccZ4S0Tf3bYb/UP92TgURsbMZMVD3eNcAHtVxrFed+4Ett2E6gnC9qkUMQyjJCaRnMwbNw/P7HkmrxP99SOvAwDOnHCmzN0iROMUlAYGgMFB63dZTtRjx0Y7UWW0C9gimirh0Hkv+7gzGTmvea6CijqKh04xWfZ7QocoGdUrI5iILtN9b5r2JJ5zf0RAhUXzQnEuIWPSJOverXBFohV/mAPXrYjO+qCx0YrSIoITRMAlEZ0P7FzU1mav8CoGFRYlCBuvcS5MbCcRnWga04SGZAMyZgZvHX9r1P/3tO1Bz2APEtEEZjbMVLCH3mBi+LEedyL60Z6jwx5HlC/zxs8DkD/O5bWjrwEAFkxYIG2fCAk4xUNnLrFsUVeFeJdOWxMHKoRs1UVNmZAnuu2wFFRUIayObFv1yoiBAWBoSF7bTge+zGN21mNwnvdEjn/WZjptr3LQcRwogET0kMGEq8OH3W1PohV/vAq4TGxn7mkiOF6d6KkU0Nlp/UxjgQ/19VaRcsD7pB5NZBDE8DgXVjC0EOREJxiGYRSMdNnashWA5b4NQ37+hCpLDGfieDGY2E4iOsHqG2w/lkdEP3JSRJ9IInpJkUvITiSACgnnO1UiulMskx0lo0smOmvXMMSKeWGJc8knrJZLsdlymkSLRu3+ljX+nWOMFfgjER0Aieihw6uITk50/njtAyb0TqDvetxwiuhuiuyycRCNWuIvERzD8D6hROcjgrAZX23NJqXNNNr62opunxXRK2kAEXaky7Zj+UX0hU0LJe6Rf5gY7kZETw2l0JHqAGCPIaJ8mTfOcqLvad+D3sHeYf8bygxlJ5lIRC8xnEKWzEgVZzuyRbRYzBZyy7GoqVMwraoSW8gxV0FFHcXDSGS4sCozE123iZVEwhojstqWPWkByD/3JBJ2jAIT0XWcTFIAieghg0R09TDh0G0fkBOdP+y17O+3Py8UwrkiQ3Tx7HKCVsYQhH/i0Tjqk/UAgCM9xZfVkBOdcMJE9NePvj7qf6+2vAoghCJ6b3ERneWhxyKx7PghypemMU1oGtOEjJnBq4dfHfa/1468hlQ6hbpEHU5rOE3RHhJCUCVkO9vRRcCX6fxVHd0hq90wFVRUJeqqLCyqKlLF2baK8Z+rr0W27Vzx0XbS7KPrOJAMyUkhg0R09XjNpWciOjnR+VFVZV9H3PQDxYiIwcuEUjptT2JTPxCExcRqa0bwSLd7EZ1lqRPlzXlTzgMA/OnAn2A6lmRlzAw2HthobTP5PCX75hUvTnQW5TK+ajwiBn2NKXcMw0DzlGYAwKaDm4b9b+N+axw0n9JM75VSQ1WkgrMdFU5UVW3rUtRUVrtMRO/qsovW6urAVTWhNNKJrqrIrszJJGfbKse/zAklJpqfOFm/SddxIBn6RBEySERXj1M4dBMlQnEuYmD94CYXnRzQYvAyodTWZo+XxkZx+0QQYWLiGEtEdyMeZkX0ShLRCeCcpnOQiCZwvPc4drXuyv59+7HtaO1rRVVFFc6ddK7CPXSPl3HAtqEoF4KxZMoSAMCmQyNE9JOTSUtPWSp9nwjBOIUsVW5QXVzwMgXToSGrbVbIsVQFUyYcMveP82+6kUvUlRHnwvpgYMBaGs5ib2Q70Us9UsWJinPPyLGg6ziQjDARvbW1FStXrkRtbS3q6+uxatUqdDuD/0ewd+9eGIaR8/aLX/wiu12u/69bt07UYWiHVxGduaDJ+ckPJt4ODNgrWwpBcS5i8FJclInoNA744sWJzvqgoUFO3SeCCANZJ7qLOBe2TdOYJqH7RISDRCyBxZMXAwBe2v9S9u9/2PcHAMD5p5wfiqKigDcnOtuGiooSjPNPOR8A8MLeF7KrMkzTxIvvvgiARPSSRKWQrUOcg6o4F2D4Fy9VmeiynOhOEV1XB67z/cgyTmVOrADD3xOqMtFVrAYphzgnNhYozmUYwkT0lStXYtu2bdiwYQOeeuopvPjii7j++uvzbj916lQcPnx42O3rX/86xowZgw984APDtv3hD384bLsrr7xS1GFohxcRva8P6OgY/jgiOMmkJQQC7hy45EQXgxcRneJcxODlfMQmk2g1AEHYuI1zMU0TLd3WBYdEdILx3mnvBQA8vfvp7N9+u+u3AIBLpl2iYpd8wQTx7oHuUcUhR0IiOjGSC6ZegKqKKhzuPozXjrwGwKoVsL9zPypjldlxQpQQOohoqgV8mW1XVADxuPUz+/KbSMhxxaiMsGAiejyub1EtVe/HeNwu5Mm+jMdi9vtEJLmE7FIfg852ZE4ojRwLuk4mSUbI2WD79u1Yv349HnzwQTQ3N+Oiiy7Cvffei3Xr1uHQoUM5HxONRtHU1DTs9vjjj+PDH/4wxox4c9TX1w/bLllGnclEqxMnLCd0Idg1LpkEamvF7le54cWBS5noYmAiupuJDFqRIQYvcS5srNCEHkHYuHXgOsVFFn1BEB+a8yEAwP++/b9IDaXQlerC07ssQf3KuVcq3DNv1MRrkIgmANiZ5/lghUUnVNGHKsIiGUvifdPfBwD4353/CwB4cseTAIBLZ1yKygpyzpUcKot7llsm8si2mWBayvE5I53oOrtvVUab5HpPGIb4dlUV2AX0mcBTNaGk81iQiBARfePGjaivr8fixYuzf1u2bBkikQheeeUVV8+xefNmbN26FatWrRr1vxtuuAHjxo3DkiVL8PDDDw8rqFTqNDaOngjOh1O0knE+KyfcioemSXEuomATGW4EXDZ3RwIuX7xMJpGIThCjYYJ4sTgX5kIfEx+DMXFJH9YJ7VkyZQkmjZmEroEu/M+O/8HPtv0MqXQKsxpn4cwJZ6rePdcYhuF6Qoky0YlcsEmjh159CAPpATz46oMAgL+Z+zcK94oQhg5u0HJxoo5smwmmKp2/siIsmFCps2HT+X5kcS4yMtEBux/Yl/FyGIPOSQsd4pxkTSi1t1v3Oo8FiQgR0VtaWjBhhO02FouhsbERLW4ULwAPPfQQ5s2bhwsuuGDY32+//Xb8/Oc/x4YNG3DVVVfhs5/9LO69996Cz5VKpdDZ2TnsFlYMw71wxV7qJlr5zR23fdDVZdfZIBc0X6ZMse7zLG4ZBgm4YmCv55EjQDpdeFt2PqI+IAgbt5noFOVC5CJiRPCpRZ8CANzy+1tw2/O3AQA+s/gzMELmnmAierGxQHEuRC4+cuZH0JBswDtt7+CKx67A3va9GFs5Fh858yOqd40QgQ5CtkoXfEeH/QVTdtuqBNPeXnmZ3yPdtjq7b3VyossWslMpO7u4nAR8FUV28/1epngS0W+55Za8xT/Z7a233gq8U319fXjsscdyutC/9rWv4cILL8Q555yDr3zlK/jyl7+Mb3/72wWf76677kJdXV32NnXq1MD7qBK3OcQkoovDbR8wF3p1tbzPOOXCKadY9wcOFN+W9dPkyeL2pxyZMMGa2EunrYipQtBEBkGMJutEL5KJTiI6kY/PNX8OjZWN2NO+B4e6DmFyzWR8evGnVe+WZ9w60bNxLiSiEw6q49X44vlfBAD8/p3fAwC+dMGXKMqlVGGi1cCA7ZBUWdxPlWgps+2RrmPZ4j1gf7GW5b5l6CwcOidW+vqG/01W27JF9FxFTcspzqmtDRgclNN2mMaCRGJeNr755pvx8Y9/vOA2M2bMQFNTE44eHf4heGhoCK2trWhyoej+8pe/RG9vL6677rqi2zY3N+OOO+5AKpVCIpHIuc3atWtx0003ZX/v7OwMtZDuVsAl0Uoc7DUt5oKmKBdxuBXRMxlyQYuiosIqFHrsmHW+KZT7T+cjghgNc6If7TkK0zTzuodJRCfy0VDZgKc+8hQ+v/7ziEfj+K/L/wtVFVXFH6gZWSe6ywklEtGJkax9z1q09bfhqbefwpVzr8Q/XfBPqneJEEUuEU1FpIqqoobsi000ahX4lIEqwbSy0nLsmKY8AX+kcKhzhAXrB6f2Jvv9KFvITiSsQq+ZjNp4IVZsVraA75xEk+1E13ksSMSTiD5+/HiMd5FJsXTpUrS3t2Pz5s1YtGgRAODZZ59FJpNBc3Nz0cc/9NBD+NCHPuSqra1bt6KhoSGvgA4AiUSi4P/DBsW5qMetgMv6gIqK8ofFubS2Wiv8Rn7eYRw7ZjmlDYMmM0QwaZItop99dv7tSEQniNEwIbBvqA/dA92oSeTOsWQRF03VdEEnRrN06lJsWr1J9W4EYtIY6+JwuDv/h9uMmcHhLuv/k2toaRkxnFgkhnuW34N7lt+jelcI0cTjlpNjcFBtHrOqOBfZhRzztS0Dw7Da6u6WJ5iGKcIi18SKLKFTVcSPYVhtdXaqnURjIroq539FhV0sURRhGgsSEZKJPm/ePKxYsQKrV6/Gpk2b8NJLL2HNmjW45pprMPlknsLBgwcxd+5cbNo0/EP/rl278OKLL+KTn/zkqOd98skn8eCDD+KNN97Arl27cP/99+POO+/EjTfeKOIwtMWrE51EdP6ceqp1v29f4e0OHrTumeBL8KOuzv7cwF7nXLBxMH48EPM0bUi4gZ2PCvUBQOcjgshFdbwa1RXWiaxQFjQ50YlSZ0qt9UHpYFf+i8nx3uMYzAzCgJEV3QmCKFNGCngqivvJdqKrcoM725ItmDrbknXcYYqwyNUvsiZWRjrRZb4nVLngVcY5qXi9wzQWJCJERAeARx99FHPnzsWll16Kyy+/HBdddBG+//3vZ/8/ODiIHTt2oLe3d9jjHn74YZxyyim47LLLRj1nRUUF7rvvPixduhQLFy7E9773Pdxzzz247bbbRB2GljBBtphoRREW4mBpQAcPFi6oyJzqzLlO8MMw3K0IoDx0sbAJpf3782+TSlkrBgA6HxHESJij9mBn/os6iehEqXNKrXVBP9CZ/4LOxsiE6gmoiFZI2S+CIDRFlYDH2u3sVCeiqxCyVQqmsidMwhRhMdKJXsqTG7naVrUSpa/PLnRbysV9wzQWJCLMl9nY2IjHHnss7/+nT58O0zRH/f3OO+/EnXfemfMxK1aswIoVK7jtY1hhotW77xbejpyf4pg0yVotlU5b57F8TnMS0cVyyinAjh3uRHQSb8Xg5nzErvXxONDYKH6fCCJMTK2bip2tO7G/M/9MFInoRKkzpeakE73AZBJzqTPXOkEQZQwTkJghT7aI1t8/+m+y2mbHLEu8c7bNjltF27IKZ8ZidlwQoLf7lr0Wx48P/10G7D3Q0TH8d5ltq3KDA+oKuQ4MjN4XUVCcS06EOdEJcUybZt2/+65VYyMXzjoLJKLzJxq1hfNCkS4koovFjROdFX8lEV0M7HxUaBw46zPIWmFIEGHBjQOXRHSi1GHCeEt3C4YyQzm3YQI7E9wJgihjRgpXst2gTmQJSyOPUYXzV0XbKo7bGWOhs3DIXhsmCtXkrq0jBJXvCVVts0K3KtpWPQ4AcqKfhET0EMKcnz09dkTCSKiYonhYpEuhGAsS0cXiJc6FRHQxuKkPQH1AEPmZWmtdTPZ35L6YpDNpEtGJkmdi9UREjSjSZhpHunPXB8g60UlEJwhClaA8UkSrrrYLDIqm3ITsfG3JduDqLByWo5Cdqy2ZhW6dbRuGvEkWFa83OdFzQiJ6CEkmbWE8X4QCE3YnTbJWIxH8KSaimyaJ6KJhr6ubwqKUiS4GpxM9k8m9Desf6gOCGA0T0Q905Z4NPNx9GGkzjVgkRiI6UbJEI1FMqrFmWvMVF8060SnOhSAIVQKeYQwX7FQK2SoiVVS0rdqBq7NwqHJyg9q2fpa1zJpEdG0gET2kOCNdcsFcocwlSvCnmAP3+HE7sorEQzGwiYxCedxsIoP6QAxTplgGnIEB4OjR3Nuw/qHzEUGMhsW55HOis79PqZmCaCQqbb8IQjZsLOTLRScnOkEQWXQR0cpROCyHtp1ioc7CYTm6wXO1req4S/31pjiXnJCIHlJIRFdPMSc6E28nTrQKKhL8mTHDut+9O399gL17rfvp02XsUflRUWFPUOQ7H7G/s/MWQRA2U+tOxrnkKSzK/s62I4hShYnj+eoDUGFRgiCy6CLglYtwWG5xLk7xUGfhcORrITMTXaeJFVUrI0p9RQY50XNCInpIKSaik/NTPMWc6BTlIp7TTrNWUHV12UXJnfT2WvUBABLRRVJsLNCkHkHkh7lvj/ceR/9Q/6j/7+uwBtCpdTSAiNKGiehF41zIiU4QhC4iGgnZ4qE4l/zo4sgu17ZltjuyHoPscWAYlBN9EhLRQwo50dUzc6Z1v2tXbhc0iejiSSatOBHAcqOPhI2P2lqgvl7abpUdzlz0XJATnSDy05BsQFWF9SE1lwOXxbmw7HSCKFXYhFKucdA72Iu2/jYA5EQnCALDBaRkEojF1LRdju5bQJ1oKauQY1jiXHRyg5fjWJB5zJGIfBf8yHEgK/9dc0hEDylMjGJRFSMhEV08M2da55GODtvt7IQJh1NJ9xAKm8woJKKTC10s7PXN1QeplF3clUR0ghiNYRgFxcN9neREJ8qDafXWRWJP+55R/9vTZv2tIdmA+mS9zN0iCEJHVEUqqGw7Gh0eLVKOgqmsQo5hiXMZ+Z6QGeeiS6RSZaX1OqhoW/a5R/YEXlgmkyRDInpIYVnQ+VzQ77xj3ZN4KI5k0hYF33579P+ZoDhrlrx9KkfY67tr1+j/sUkmEm/FMnu2db9z5+j/sZoBySQwbpy8fSKIMMEE8r3te0f9j4mH0+roREaUNjMbrFnx3a2jZ2R3t1l/m9EwQ+o+EQShKarcoCPbk922quPWJc5F1jGHJc4F0GdlhMqJFZmoPPfIbjssk0mSIRE9pMyaZa3o6OoCWlqG/6+11bqx7QhxMPEwl4jORF3qA7EUcqLvOWlmo8kksZx+unVfbBzQCjCCyM2sButCsfPE8Jko0zSxq9UaRLMa6WJClDYzG60L+pGeI+gZ6Bn2v3faLHcIiegEQQAoTyF7ZHvlGOciq12ncK67eKjLxEqpvyd0a1tFnAsBgET00JJI2MLgSOGKuUEnT5Y/MVduMPFwpAPXNElEl0UhEX3HDuue9RMhBvb67t8P9PUN/x87P1EfEER+Zo+1ZmR3tQ1fUtPS3YKewR5EjAhOazhNxa4RhDTqk/VoSDYAsEVzBonoBEEMQ6UTtRwF/HJzHZMT3Vu7stsux4mskW3LjnOJx8W3FxJIRA8xc+ZY90woZDBBl7mkCXHkc+AePQp0d1urBcgFLRZngdeRsLHBxgohhnHjrMKtpjl6MoNEdIIoDnOZj3SiMxf6tLppiEfpwytR+jA3OotvYZCIThDEMHQRsktdRMvXlqr8axVOdN1FdGdfyMxEV1ncU6WQrYuALzvOhZaUZyERPcQwUYpEdHXkE9FZH0ydaq0aIMTBBPKjR4cXeB0ctIX1uXPl71c5YRj2WNi+ffj/SEQniOLMbrQu2Dtbd8J0FDphIjpzqhNEqcMmlN4+MfyDFfud5aYTBFHmlJOQ5USVgD/ShRqLyWtbdSa67nEuqt6PiYRd0DMeByoq5LWtS6RKqa9EcU4g5SrEWKaQiB5i5s+37l9/ffjf33rLuicRXTzOOJehIfvvrE9YHxHiGDPGdqM7x8KePVafVFUBU6ao2bdyYsEC6/4vfxn+d4rUIYjizGiYAQMGuge60dJtFzrZccIaQCwznSBKnfnjrA9Obxx9I/u33sHe7ITSmRPOVLJfBEFoRrk6UVW2rQrVInqYnOgy3xOGYbddTuNAl5UoMtqOOORiEtGzkIgeYs45x7rfsmX4e/rVV637s8+Wv0/lxvTp1qqpVGq4A5cJidQHcmAC7muv2X97803r/vTTh5//CTGw97pTRG9vB/bts36eN0/6LhFEaEjEElm3+V+O2IOI/XzWxLOU7BdByIa9118/as+Kbz+2HSZMjK8aj4ljJqraNYIgdEIXJ3q5xLmoRMXrHaY4Fx1c2eXkBi/XtklEz0LSUog56yxrBc2JE8CBA9bfurrsCAsmshPiiESAc8+1ft682f47iehyyeWC/r//s+5Z/xBiWbjQut+61f7bli3W/WmnAY2NsveI8MM3vvENXHDBBaiqqkJ9fb2rx5imiVtvvRWTJk1CZWUlli1bhp0jqi23trZi5cqVqK2tRX19PVatWoXu7m4BRxBezmmyLtqvHn41+7etLVsBAAubFirYI4KQz1kTLBF9+7HtGMpYS/yYK51c6ARBZCmnSAUnzpxQ2QK+KlQXFtU9zkVVJrqzbZWrQWglihxIRM9CInqISSbtuBAmVr32mvX+njwZmDBB3b6VE4sWWfdMRM9k7FgRJu4SYmETRps22X/785+t+8WL5e9POcLe6wcOWPn0gH1eoomM8DAwMIC///u/x2c+8xnXj/nWt76F7373u3jggQfwyiuvoLq6GsuXL0d/f392m5UrV2Lbtm3YsGEDnnrqKbz44ou4/vrrRRxCaGEi+tYjWwEALd0taOlugQEjKywSRKlzWsNpqK6oRiqdyuagv3bEWmZGIjpBEFmcYpJskVMXN3i5ONGdfS0ri52c6N7aLidHti4rUWS3feqpctvTGBLRQ85551n3f/iDdc9ERHKhy4OJtH/6k3W/dSvQ02Od4ygHWg4XXmjdv/mmVVzUNG0nOhsjhFhqa63VMQDwwgvWPesDNtFE6M/Xv/51fPGLX8RZZ7kTbU3TxH/8x3/gq1/9Kv76r/8aCxYswI9//GMcOnQIv/71rwEA27dvx/r16/Hggw+iubkZF110Ee69916sW7cOhw4dEng04eKcSdaFe/Mha0Z2y2FrFmr22NmojpeJ24woeyJGBIsmWxeNl/a9BAD4wz7rQ+6SKUuU7RdBEJpRW2v/bBjq2pbt/K2rs38eWexTNKecYt3LnrRwioXO4xdJGDPRYzH57wlVInplpT3uy8kNrkJEnzzZur/hBjnthQAS0UPOsmXW/YYN1v3vfmfdv+99avanHGGv9ebNloD7zDPW75dcIrdweTkzbhxwxhnWz3/4g7USoLXV+oznUgskOPD+91v3zz4LpNPA739v/X7BBer2iRDLnj170NLSgmXsYgSgrq4Ozc3N2LhxIwBg48aNqK+vx2LHspBly5YhEonglVdeyfvcqVQKnZ2dw26lTPOUZkSNKHa37caetj3YsNu6sL/31Pcq3jOCkMsl0y4BADy39zl0pjqx+bA1sXTxtIsV7hVBlAb33Xcfpk+fjmQyiebmZmxyLuPMwS9+8QvMnTsXyWQSZ511Fn7zm99I2tMixOPAXXcBY8cCH/qQ3LYvvBD4u78DrrlGvlPkYx+zHELf+Y78yYN//meryBH7siuLaBSYdbLA+j/+o5w2wxTn4hSyZb8nVBUWdRY1Lae6BKy9RAKoqJDT5ksvAf/7v8Bf/7Wc9kIAiegh59JLrfvXXgP27LEdoMuXq9uncqOpyXL+myawfr0tHLK+IeTAJjN+/WvrBgCXXTY8OpAQC+uD3/4W2LjRqtdQX2+vFCBKj5aWFgDAxInDi/1NnDgx+7+WlhZMGJEvFovF0NjYmN0mF3fddRfq6uqyt6lTp3Lee72oS9bhgqnWjNP6Xevx9O6nAQDLZ9EFnSgv3neadTF5bu9zeGHvC8iYGcxsmImpdaV9DiAI0fzsZz/DTTfdhNtuuw1btmzB2WefjeXLl+Moy+Ebwcsvv4yPfOQjWLVqFV599VVceeWVuPLKK/HGG29I3vM83HKL5WBqbpbbbmUl8ItfAP/93/IdU+eeay09v+kmue0CwKc/bS37VeGO+cMfgN27rUJLMmDuc8OQ7+72ChORZa+KAGxRV0U+vyoBX2WkioqJg+nTgcsvl9deCCARPeRMmACcf7718+WXA319wNSptiuXkMMVV1j3X/+6PTn/gQ+o259y5Nprrfuf/Qy4/37r5yuvVLY7Zclf/RXQ0AC8+y6wcqX1txUraEWGam655RYYhlHw9tZbb6nezVGsXbsWHR0d2dv+/ftV75JwPjDLunD887P/jO3HtyNqRHHpaTQjS5QX559yPmoTtWjpbsHH/+fjAIDlM2kyiSCCcs8992D16tX4xCc+gfnz5+OBBx5AVVUVHn744Zzb/+d//idWrFiBL33pS5g3bx7uuOMOnHvuufiv//ovyXteANnOW0INTU3AjBny2mNOdGdsiK6oilQB1AnZzjbLMc6lXOohaAqJ6CUAiydiGsinPqX/ub7U+OQnrRU1u3dbMRbvex8wZ47qvSovliyxilsODAAtLVZk3t/8jeq9Ki+qqoBVq6yf9+2z7j/5SXX7Q1jcfPPN2L59e8HbDJ9fTJqamgAAR44cGfb3I0eOZP/X1NQ0yuU2NDSE1tbW7Da5SCQSqK2tHXYrda5dcC1ikRja+tsAAH8z72/QUNmgeK8IQi7JWBIfO/tjAIDWvlYAwOpFq1XuEkGEnoGBAWzevHlY/FokEsGyZcuy8Wsj2bhx47DtAWD58uV5twfKL4qNKFEmTbJiZMKwClKlsKpD2+VU3HPs2OH3hBJIRC8B/uEfgKuvtn6+6CI1q7vKnWnTgHvusRy3U6YA992neo/KD8MAvvc9ywmdSAAPPGBFiRByWbsWOPts6+c1a+ycdEId48ePx9y5cwve4j6Xqp522mloamrCM458zM7OTrzyyitYunQpAGDp0qVob2/H5s2bs9s8++yzyGQyaJa9BFtzptZNxXcu+w4iRgTT66fj7r+6W/UuEYQSbr34ViyYuAAGDPzze/4ZC5sWqt4lggg1x48fRzqdLhi/NpKWlhZP2wPlF8VGlCgTJwIvvwzoUgOgEBdfbOXky8qLd9LcbH0JP+88+W1feqlV5Fd2XYKqKqsOw7JlwPjxctteuhS44w7g3/9dbrvEMGiRfQkQiQDr1lkRFvX15EJXxZo1Vq2XZFJenQdiOOefDxw5YrnRVUSzEUBjI7BlC9DdbX2uIcLFvn370Nrain379iGdTmPr1q0AgFmzZmHMSefF3Llzcdddd+Fv/uZvYBgGvvCFL+Bf//VfMXv2bJx22mn42te+hsmTJ+PKk3lK8+bNw4oVK7B69Wo88MADGBwcxJo1a3DNNddgMqv4TmT5XPPn8MlzP4l4NI5YhD6mEeXJuKpx2PqprehMdaIuWad6dwiCcMnatWtxk8PR1dnZSUI6EU6WLFG9B+6YMMHKyVfBRz9q5aeqyGP/zneAb35TvvBiGMD//I/cNhnRKPDVr6ppm8hC385KiAZa8a0cFdcPYjgVFTSJoZpIhAT0sHLrrbfiRz/6Ufb3c845BwDw3HPP4ZJLLgEA7NixAx0dHdltvvzlL6OnpwfXX3892tvbcdFFF2H9+vVIJpPZbR599FGsWbMGl156KSKRCK666ip897vflXNQIaSqokr1LhCEcgzDIAGdIDgxbtw4RKPRgvFrI2lqavK0PWBFsSUSieA7TBBEOFApgNCXfkIBhmmapuqdkE1nZyfq6urQ0dFRFhmrBEEQRDih65UNvRYEQRBEGND1etXc3IwlS5bg3nvvBQBkMhmceuqpWLNmDW655ZZR21999dXo7e3Fk08+mf3bBRdcgAULFuCBBx5w1aaurwVBEARBOHF7vSInOkEQBEEQBEEQBEGUMDfddBM+9rGPYfHixViyZAn+4z/+Az09PfjEJz4BALjuuuswZcoU3HXXXQCAz3/+87j44ovxne98B1dccQXWrVuH//u//8P3v/99lYdBEARBEMogEZ0gCIIgCIIgCIIgSpirr74ax44dw6233oqWlhYsXLgQ69evzxYP3bdvHyKRSHb7Cy64AI899hi++tWv4v/9v/+H2bNn49e//jXOPPNMVYdAEARBEEqhOBdaVkYQBEFoCl2vbOi1IAiCIMIAXa9s6LUgCIIgwoDb61Uk738IgiAIgiAIgiAIgiAIgiAIoswhEZ0gCIIgCIIgCIIgCIIgCIIg8kAiOkEQBEEQBEEQBEEQBEEQBEHkgUR0giAIgiAIgiAIgiAIgiAIgsgDiegEQRAEQRAEQRAEQRAEQRAEkQcS0QmCIAiCIAiCIAiCIAiCIAgiDySiEwRBEARBEARBEARBEARBEEQeSEQnCIIgCIIgCIIgCIIgCIIgiDyQiE4QBEEQBEEQBEEQBEEQBEEQeSARnSAIgiAIgiAIgiAIgiAIgiDyQCI6QRAEQRAEQRAEQRAEQRAEQeQhpnoHVGCaJgCgs7NT8Z4QBEEQRH7YdYpdt8oZunYTBEEQYYCu3TZ07SYIgiDCgNtrd1mK6F1dXQCAqVOnKt4TgiAIgihOV1cX6urqVO+GUujaTRAEQYQJunbTtZsgCIIIF8Wu3YZZhlPkmUwGhw4dQk1NDQzDCPRcnZ2dmDp1Kvbv34/a2lpOeygXOgb1hH3/AToGHQj7/gPhPwbe+2+aJrq6ujB58mREIuWdwEbX7uHQMagn7PsP0DHoQNj3H6BjGAldu214XruB8L/Xwr7/QPiPIez7D9Ax6EDY9x+gYxiJ22t3WTrRI5EITjnlFK7PWVtbG9o3HoOOQT1h33+AjkEHwr7/QPiPgef+l7uLjUHX7tzQMagn7PsP0DHoQNj3H6BjcELXbgsR124g/O+1sO8/EP5jCPv+A3QMOhD2/QfoGJy4uXaX99Q4QRAEQRAEQRAEQRAEQRAEQRSARHSCIAiCIAiCIAiCIAiCIAiCyAOJ6AFJJBK47bbbkEgkVO+Kb+gY1BP2/QfoGHQg7PsPhP8Ywr7/5UIp9BMdg3rCvv8AHYMOhH3/AToGQh5h76ew7z8Q/mMI+/4DdAw6EPb9B+gY/FKWhUUJgiAIgiAIgiAIgiAIgiAIwg3kRCcIgiAIgiAIgiAIgiAIgiCIPJCIThAEQRAEQRAEQRAEQRAEQRB5IBGdIAiCIAiCIAiCIAiCIAiCIPJAIjpBEARBEARBEARBEARBEARB5IFEdBfcd999mD59OpLJJJqbm7Fp06aC2//iF7/A3LlzkUwmcdZZZ+E3v/mNpD3Nj5djeOSRR2AYxrBbMpmUuLfDefHFF/HBD34QkydPhmEY+PWvf130Mc8//zzOPfdcJBIJzJo1C4888ojw/SyE12N4/vnnR/WBYRhoaWmRs8MjuOuuu3DeeeehpqYGEyZMwJVXXokdO3YUfZxOY8HPMeg0Fu6//34sWLAAtbW1qK2txdKlS/Hb3/624GN0ev0B78eg0+ufi29+85swDANf+MIXCm6nWz+UC3Ttpmt3UOjarX4s0LVb/bmIrt169EO5QNduunYHha7d6scCXbvVn4vo2i2uH0hEL8LPfvYz3HTTTbjtttuwZcsWnH322Vi+fDmOHj2ac/uXX34ZH/nIR7Bq1Sq8+uqruPLKK3HllVfijTfekLznNl6PAQBqa2tx+PDh7O3dd9+VuMfD6enpwdlnn4377rvP1fZ79uzBFVdcgfe9733YunUrvvCFL+CTn/wknn76acF7mh+vx8DYsWPHsH6YMGGCoD0szAsvvIAbbrgBf/rTn7BhwwYMDg7isssuQ09PT97H6DYW/BwDoM9YOOWUU/DNb34Tmzdvxv/93//h/e9/P/76r/8a27Zty7m9bq8/4P0YAH1e/5H8+c9/xve+9z0sWLCg4HY69kM5QNdu9eOFrt107eYBXbvVn4vo2q1HP5QDdO1WP17o2k3Xbh7QtVv9uYiu3QL7wSQKsmTJEvOGG27I/p5Op83Jkyebd911V87tP/zhD5tXXHHFsL81Nzebn/rUp4TuZyG8HsMPf/hDs66uTtLeeQOA+fjjjxfc5stf/rJ5xhlnDPvb1VdfbS5fvlzgnrnHzTE899xzJgCzra1Nyj555ejRoyYA84UXXsi7jY5jwYmbY9B5LJimaTY0NJgPPvhgzv/p/vozCh2Drq9/V1eXOXv2bHPDhg3mxRdfbH7+85/Pu21Y+qHUoGu3XtC1Ww/o2q0HdO1WA1279Yeu3XpB1249oGu3HtC1Ww06XrvJiV6AgYEBbN68GcuWLcv+LRKJYNmyZdi4cWPOx2zcuHHY9gCwfPnyvNuLxs8xAEB3dzemTZuGqVOnFp2x0g3d+iAICxcuxKRJk/BXf/VXeOmll1TvTpaOjg4AQGNjY95tdO8HN8cA6DkW0uk01q1bh56eHixdujTnNrq//m6OAdDz9b/hhhtwxRVXjHp9c6F7P5QidO3Wa7y4Rbc+CAJdu8VB12610LVbj34oRejardd4cYtufRAEunaLg67daqFrN99+IBG9AMePH0c6ncbEiROH/X3ixIl5M7JaWlo8bS8aP8cwZ84cPPzww/if//kf/PSnP0Umk8EFF1yAAwcOyNjlwOTrg87OTvT19SnaK29MmjQJDzzwAH71q1/hV7/6FaZOnYpLLrkEW7ZsUb1ryGQy+MIXvoALL7wQZ555Zt7tdBsLTtweg25j4fXXX8eYMWOQSCTw6U9/Go8//jjmz5+fc1tdX38vx6Db6w8A69atw5YtW3DXXXe52l7Xfihl6Nqtz3jxAl27xULXbrp2B4Gu3Xr0QylD1259xosX6NotFrp207U7CHTtFtMPMa7PRpQES5cuHTZDdcEFF2DevHn43ve+hzvuuEPhnpUPc+bMwZw5c7K/X3DBBdi9ezf+/d//HT/5yU8U7pk1G/jGG2/gj3/8o9L9CILbY9BtLMyZMwdbt25FR0cHfvnLX+JjH/sYXnjhhbwXQx3xcgy6vf779+/H5z//eWzYsEGrQisEAeg3XsoRunaLha7d6qBrN0GIQbfxUo7QtVssdO1WB127xUAiegHGjRuHaDSKI0eODPv7kSNH0NTUlPMxTU1NnrYXjZ9jGElFRQXOOecc7Nq1S8QucidfH9TW1qKyslLRXgVnyZIlyi+ga9aswVNPPYUXX3wRp5xySsFtdRsLDC/HMBLVYyEej2PWrFkAgEWLFuHPf/4z/vM//xPf+973Rm2r6+vv5RhGovr137x5M44ePYpzzz03+7d0Oo0XX3wR//Vf/4VUKoVoNDrsMbr2QylD124L1ePFK3TtFgddu+naHRS6duvRD6UMXbstVI8Xr9C1Wxx07aZrd1Do2i2mHyjOpQDxeByLFi3CM888k/1bJpPBM888kzdLaOnSpcO2B4ANGzYUzB4SiZ9jGEk6ncbrr7+OSZMmidpNrujWB7zYunWrsj4wTRNr1qzB448/jmeffRannXZa0cfo1g9+jmEkuo2FTCaDVCqV83+6vf75KHQMI1H9+l966aV4/fXXsXXr1uxt8eLFWLlyJbZu3TrqQg6Epx9KCbp2W6geL17RrQ94QdfuYNC1W89xQNdugjd07bZQPV68olsf8IKu3cGga7ee44Cu3ZzgWqa0BFm3bp2ZSCTMRx55xHzzzTfN66+/3qyvrzdbWlpM0zTNj370o+Ytt9yS3f6ll14yY7GYeffdd5vbt283b7vtNrOiosJ8/fXXVR2C52P4+te/bj799NPm7t27zc2bN5vXXHONmUwmzW3btinZ/66uLvPVV181X331VROAec8995ivvvqq+e6775qmaZq33HKL+dGPfjS7/TvvvGNWVVWZX/rSl8zt27eb9913nxmNRs3169cr2X/T9H4M//7v/27++te/Nnfu3Gm+/vrr5uc//3kzEomYv//975Xs/2c+8xmzrq7OfP75583Dhw9nb729vdltdB8Lfo5Bp7Fwyy23mC+88IK5Z88e87XXXjNvueUW0zAM83e/+13Ofdft9TdN78eg0+ufj5FVwsPQD+UAXbvVjxe6dtO1mwd07VZ/LqJrtx79UA7QtVv9eKFrN127eUDXbvXnIrp2i+sHEtFdcO+995qnnnqqGY/HzSVLlph/+tOfsv+7+OKLzY997GPDtv/5z39unn766WY8HjfPOOMM83//938l7/FovBzDF77whey2EydONC+//HJzy5YtCvba4rnnnjMBjLqxff7Yxz5mXnzxxaMes3DhQjMej5szZswwf/jDH0rf75H74+UY/u3f/s2cOXOmmUwmzcbGRvOSSy4xn332WTU7b5o59x3AsNdV97Hg5xh0Ggv/+I//aE6bNs2Mx+Pm+PHjzUsvvTR7ETRN/V9/0/R+DDq9/vkYeTEPQz+UC3Ttpmt3UOjarX4s0LVb/bmIrt169EO5QNduunYHha7d6scCXbvVn4vo2i2uHwzTNE3/PnaCIAiCIAiCIAiCIAiCIAiCKF0oE50gCIIgCIIgCIIgCIIgCIIg8kAiOkEQBEEQBEEQBEEQBEEQBEHkgUR0giAIgiAIgiAIgiAIgiAIgsgDiegEQRAEQRAEQRAEQRAEQRAEkQcS0QmCIAiCIAiCIAiCIAiCIAgiDySiEwRBEARBEARBEARBEARBEEQeSEQnCIIgCIIgCIIgCIIgCIIgiDyQiE4QBEEQBEEQBEEQBEEQBEEQeSARnSAIgiAIgiAIgiAIgiAIgiDyQCI6QRAEQRAEQRAEQRAEQRAEQeSBRHSCIAiCIAiCIAiCIAiCIAiCyAOJ6ARBEARBEARBEARBEARBEASRh/8f31SAbQpDoggAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -349,7 +70363,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,