{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf15078f-bfa4-442d-97ae-98018bf2ab17",
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "operator torchvision::nms does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mRuntimeError\u001b[39m                              Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mwarnings\u001b[39;00m\n\u001b[32m      8\u001b[39m warnings.filterwarnings(\u001b[33m'\u001b[39m\u001b[33mignore\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mhtb_ai_library\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m set_reproducibility\n\u001b[32m     11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mhtb_ai_library\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdata\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_mnist_loaders\n\u001b[32m     12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mhtb_ai_library\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodels\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SimpleLeNet\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/htb_ai_library/__init__.py:16\u001b[39m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m      7\u001b[39m     set_reproducibility,\n\u001b[32m      8\u001b[39m     save_model,\n\u001b[32m   (...)\u001b[39m\u001b[32m     12\u001b[39m     HTB_PALETTE, get_color, get_color_palette\n\u001b[32m     13\u001b[39m )\n\u001b[32m     15\u001b[39m \u001b[38;5;66;03m# data subpackage\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdata\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m     17\u001b[39m     get_mnist_loaders,\n\u001b[32m     18\u001b[39m     download_sms_spam_dataset,\n\u001b[32m     19\u001b[39m     mnist_denormalize,\n\u001b[32m     20\u001b[39m     cifar_normalize,\n\u001b[32m     21\u001b[39m     load_adult_census,\n\u001b[32m     22\u001b[39m     get_cifar10_loaders,\n\u001b[32m     23\u001b[39m     get_cifar10_transform,\n\u001b[32m     24\u001b[39m )\n\u001b[32m     26\u001b[39m \u001b[38;5;66;03m# models subpackage\u001b[39;00m\n\u001b[32m     27\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodels\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m     28\u001b[39m     SimpleLeNet,\n\u001b[32m     29\u001b[39m     SimpleCNN,\n\u001b[32m   (...)\u001b[39m\u001b[32m     34\u001b[39m     AttackModel,\n\u001b[32m     35\u001b[39m )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/htb_ai_library/data/__init__.py:5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[33;03mData subpackage providing loaders and dataset utilities.\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmnist\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_mnist_loaders, mnist_denormalize\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01msms\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m download_sms_spam_dataset\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtransforms\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m cifar_normalize\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/htb_ai_library/data/mnist.py:11\u001b[39m\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorch\u001b[39;00m\n\u001b[32m     10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorch\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdata\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DataLoader\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchvision\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m datasets, transforms\n\u001b[32m     14\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mget_mnist_loaders\u001b[39m(\n\u001b[32m     15\u001b[39m     batch_size: \u001b[38;5;28mint\u001b[39m = \u001b[32m128\u001b[39m,\n\u001b[32m     16\u001b[39m     data_dir: \u001b[38;5;28mstr\u001b[39m = \u001b[33m\"\u001b[39m\u001b[33m./data\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m     19\u001b[39m     seed: Optional[\u001b[38;5;28mint\u001b[39m] = \u001b[32m1337\u001b[39m,\n\u001b[32m     20\u001b[39m ) -> Tuple[DataLoader, DataLoader]:\n\u001b[32m     21\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     22\u001b[39m \u001b[33;03m    Create MNIST data loaders with optional normalization and deterministic shuffling.\u001b[39;00m\n\u001b[32m     23\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m     39\u001b[39m \u001b[33;03m        Training and test data loaders.\u001b[39;00m\n\u001b[32m     40\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/torchvision/__init__.py:10\u001b[39m\n\u001b[32m      7\u001b[39m \u001b[38;5;66;03m# Don't re-order these, we need to load the _C extension (done when importing\u001b[39;00m\n\u001b[32m      8\u001b[39m \u001b[38;5;66;03m# .extensions) before entering _meta_registrations.\u001b[39;00m\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mextension\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _HAS_OPS  \u001b[38;5;66;03m# usort:skip\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchvision\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _meta_registrations, datasets, io, models, ops, transforms, utils  \u001b[38;5;66;03m# usort:skip\u001b[39;00m\n\u001b[32m     12\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m     13\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mversion\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m __version__  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/torchvision/_meta_registrations.py:163\u001b[39m\n\u001b[32m    153\u001b[39m     torch._check(\n\u001b[32m    154\u001b[39m         grad.dtype == rois.dtype,\n\u001b[32m    155\u001b[39m         \u001b[38;5;28;01mlambda\u001b[39;00m: (\n\u001b[32m   (...)\u001b[39m\u001b[32m    158\u001b[39m         ),\n\u001b[32m    159\u001b[39m     )\n\u001b[32m    160\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m grad.new_empty((batch_size, channels, height, width))\n\u001b[32m--> \u001b[39m\u001b[32m163\u001b[39m \u001b[38;5;129;43m@torch\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlibrary\u001b[49m\u001b[43m.\u001b[49m\u001b[43mregister_fake\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtorchvision::nms\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m    164\u001b[39m \u001b[38;5;28;43;01mdef\u001b[39;49;00m\u001b[38;5;250;43m \u001b[39;49m\u001b[34;43mmeta_nms\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdets\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscores\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miou_threshold\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m    165\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_check\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdets\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdim\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mboxes should be a 2d tensor, got \u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mdets\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdim\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43mD\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m    166\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_check\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdets\u001b[49m\u001b[43m.\u001b[49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mboxes should have 4 elements in dimension 1, got \u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mdets\u001b[49m\u001b[43m.\u001b[49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/torch/library.py:1073\u001b[39m, in \u001b[36mregister_fake.<locals>.register\u001b[39m\u001b[34m(func)\u001b[39m\n\u001b[32m   1071\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m   1072\u001b[39m     use_lib = lib\n\u001b[32m-> \u001b[39m\u001b[32m1073\u001b[39m \u001b[43muse_lib\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_register_fake\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1074\u001b[39m \u001b[43m    \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacklevel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstacklevel\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_override\u001b[49m\u001b[43m=\u001b[49m\u001b[43mallow_override\u001b[49m\n\u001b[32m   1075\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1076\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m func\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/torch/library.py:203\u001b[39m, in \u001b[36mLibrary._register_fake\u001b[39m\u001b[34m(self, op_name, fn, _stacklevel, allow_override)\u001b[39m\n\u001b[32m    200\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    201\u001b[39m     func_to_register = fn\n\u001b[32m--> \u001b[39m\u001b[32m203\u001b[39m handle = \u001b[43mentry\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfake_impl\u001b[49m\u001b[43m.\u001b[49m\u001b[43mregister\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    204\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfunc_to_register\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlib\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_override\u001b[49m\u001b[43m=\u001b[49m\u001b[43mallow_override\u001b[49m\n\u001b[32m    205\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    206\u001b[39m \u001b[38;5;28mself\u001b[39m._registration_handles.append(handle)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/torch/_library/fake_impl.py:50\u001b[39m, in \u001b[36mFakeImplHolder.register\u001b[39m\u001b[34m(self, func, source, lib, allow_override)\u001b[39m\n\u001b[32m     44\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.kernel \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m     45\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m     46\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mregister_fake(...): the operator \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.qualname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     47\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33malready has an fake impl registered at \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     48\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.kernel.source\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     49\u001b[39m     )\n\u001b[32m---> \u001b[39m\u001b[32m50\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_C\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_dispatch_has_kernel_for_dispatch_key\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mqualname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mMeta\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m:\n\u001b[32m     51\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m     52\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mregister_fake(...): the operator \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.qualname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     53\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33malready has an DispatchKey::Meta implementation via a \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   (...)\u001b[39m\u001b[32m     56\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mregister_fake.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     57\u001b[39m     )\n\u001b[32m     59\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch._C._dispatch_has_kernel_for_dispatch_key(\n\u001b[32m     60\u001b[39m     \u001b[38;5;28mself\u001b[39m.qualname, \u001b[33m\"\u001b[39m\u001b[33mCompositeImplicitAutograd\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     61\u001b[39m ):\n",
      "\u001b[31mRuntimeError\u001b[39m: operator torchvision::nms does not exist"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from htb_ai_library.core import set_reproducibility\n",
    "from htb_ai_library.data import get_mnist_loaders\n",
    "from htb_ai_library.models import SimpleLeNet\n",
    "from htb_ai_library.training import train_model\n",
    "from htb_ai_library.utils import save_model, load_model\n",
    "from htb_ai_library.visualization import use_htb_style\n",
    "\n",
    "use_htb_style()\n",
    "set_reproducibility(1337)\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "output_dir = Path('output')\n",
    "output_dir.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38071a4d-e798-4956-9f69-356d4c5b5ab5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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