{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e40a88b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import torch\n",
    "from torch import nn\n",
    "from importlib.util import find_spec\n",
    "if find_spec(\"text_recognizer\") is None:\n",
    "    import sys\n",
    "    sys.path.append('..')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f40fc669-829c-4de8-83ed-475fc6a0b8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class T:\n",
    "    def __init__(self):\n",
    "        self.network = nn.Linear(1, 1)\n",
    "        \n",
    "    def get(self):\n",
    "        return getattr(self, \"network\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2bedf96-5388-4c7a-a048-1b97041cbedc",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = T()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6fbe3be-2a9f-4050-a397-7ad982d6cd05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object Module.parameters at 0x7f29ad6d6120>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.get().parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38fb3d9d-a163-4b72-981f-f31b51be39f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hydra import compose, initialize\n",
    "from omegaconf import OmegaConf\n",
    "from hydra.utils import instantiate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "74780b21-3313-452b-b580-703cac878416",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "encoder:\n",
      "  _target_: text_recognizer.networks.vqvae.encoder.Encoder\n",
      "  in_channels: 1\n",
      "  hidden_dim: 32\n",
      "  channels_multipliers:\n",
      "  - 1\n",
      "  - 2\n",
      "  - 4\n",
      "  - 4\n",
      "  - 4\n",
      "  dropout_rate: 0.25\n",
      "decoder:\n",
      "  _target_: text_recognizer.networks.vqvae.decoder.Decoder\n",
      "  out_channels: 1\n",
      "  hidden_dim: 32\n",
      "  channels_multipliers:\n",
      "  - 4\n",
      "  - 4\n",
      "  - 4\n",
      "  - 2\n",
      "  - 1\n",
      "  dropout_rate: 0.25\n",
      "_target_: text_recognizer.networks.vqvae.vqvae.VQVAE\n",
      "hidden_dim: 128\n",
      "embedding_dim: 32\n",
      "num_embeddings: 1024\n",
      "decay: 0.99\n",
      "\n",
      "{'encoder': {'_target_': 'text_recognizer.networks.vqvae.encoder.Encoder', 'in_channels': 1, 'hidden_dim': 32, 'channels_multipliers': [1, 2, 4, 4, 4], 'dropout_rate': 0.25}, 'decoder': {'_target_': 'text_recognizer.networks.vqvae.decoder.Decoder', 'out_channels': 1, 'hidden_dim': 32, 'channels_multipliers': [4, 4, 4, 2, 1], 'dropout_rate': 0.25}, '_target_': 'text_recognizer.networks.vqvae.vqvae.VQVAE', 'hidden_dim': 128, 'embedding_dim': 32, 'num_embeddings': 1024, 'decay': 0.99}\n"
     ]
    }
   ],
   "source": [
    "# context initialization\n",
    "with initialize(config_path=\"../training/conf/network/\", job_name=\"test_app\"):\n",
    "    cfg = compose(config_name=\"vqvae\")\n",
    "    print(OmegaConf.to_yaml(cfg))\n",
    "    print(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "205a03e8-7aa1-407f-afa5-92693715b677",
   "metadata": {},
   "outputs": [],
   "source": [
    "net = instantiate(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c74384f0-754e-4c29-8f06-339372d6e4c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ebab599-2497-42f8-b54b-1663ee66fde9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "Layer (type:depth-idx)                   Output Shape              Param #\n",
      "==========================================================================================\n",
      "├─Encoder: 1-1                           [-1, 128, 18, 20]         --\n",
      "|    └─Sequential: 2-1                   [-1, 128, 18, 20]         --\n",
      "|    |    └─Conv2d: 3-1                  [-1, 32, 576, 640]        320\n",
      "|    |    └─Conv2d: 3-2                  [-1, 32, 288, 320]        16,416\n",
      "|    |    └─Mish: 3-3                    [-1, 32, 288, 320]        --\n",
      "|    |    └─Conv2d: 3-4                  [-1, 64, 144, 160]        32,832\n",
      "|    |    └─Mish: 3-5                    [-1, 64, 144, 160]        --\n",
      "|    |    └─Conv2d: 3-6                  [-1, 128, 72, 80]         131,200\n",
      "|    |    └─Mish: 3-7                    [-1, 128, 72, 80]         --\n",
      "|    |    └─Conv2d: 3-8                  [-1, 128, 36, 40]         262,272\n",
      "|    |    └─Mish: 3-9                    [-1, 128, 36, 40]         --\n",
      "|    |    └─Conv2d: 3-10                 [-1, 128, 18, 20]         262,272\n",
      "|    |    └─Mish: 3-11                   [-1, 128, 18, 20]         --\n",
      "|    |    └─Residual: 3-12               [-1, 128, 18, 20]         164,352\n",
      "|    |    └─Residual: 3-13               [-1, 128, 18, 20]         164,352\n",
      "├─Conv2d: 1-2                            [-1, 32, 18, 20]          4,128\n",
      "├─VectorQuantizer: 1-3                   [-1, 32, 18, 20]          --\n",
      "├─Conv2d: 1-4                            [-1, 128, 18, 20]         4,224\n",
      "├─Decoder: 1-5                           [-1, 1, 576, 640]         --\n",
      "|    └─Sequential: 2-2                   [-1, 1, 576, 640]         --\n",
      "|    |    └─Residual: 3-14               [-1, 128, 18, 20]         164,352\n",
      "|    |    └─Residual: 3-15               [-1, 128, 18, 20]         164,352\n",
      "|    |    └─ConvTranspose2d: 3-16        [-1, 128, 36, 40]         262,272\n",
      "|    |    └─Mish: 3-17                   [-1, 128, 36, 40]         --\n",
      "|    |    └─ConvTranspose2d: 3-18        [-1, 128, 72, 80]         262,272\n",
      "|    |    └─Mish: 3-19                   [-1, 128, 72, 80]         --\n",
      "|    |    └─ConvTranspose2d: 3-20        [-1, 64, 144, 160]        131,136\n",
      "|    |    └─Mish: 3-21                   [-1, 64, 144, 160]        --\n",
      "|    |    └─ConvTranspose2d: 3-22        [-1, 32, 288, 320]        32,800\n",
      "|    |    └─Mish: 3-23                   [-1, 32, 288, 320]        --\n",
      "|    |    └─ConvTranspose2d: 3-24        [-1, 32, 576, 640]        16,416\n",
      "|    |    └─Mish: 3-25                   [-1, 32, 576, 640]        --\n",
      "|    |    └─Normalize: 3-26              [-1, 32, 576, 640]        64\n",
      "|    |    └─Mish: 3-27                   [-1, 32, 576, 640]        --\n",
      "|    |    └─Conv2d: 3-28                 [-1, 1, 576, 640]         289\n",
      "==========================================================================================\n",
      "Total params: 2,076,321\n",
      "Trainable params: 2,076,321\n",
      "Non-trainable params: 0\n",
      "Total mult-adds (G): 17.68\n",
      "==========================================================================================\n",
      "Input size (MB): 1.41\n",
      "Forward/backward pass size (MB): 355.17\n",
      "Params size (MB): 7.92\n",
      "Estimated Total Size (MB): 364.49\n",
      "==========================================================================================\n"
     ]
    }
   ],
   "source": [
    "summary(net, (1, 576, 640), device=\"cpu\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ba3f405-5948-465d-a7b8-459c84345034",
   "metadata": {},
   "outputs": [],
   "source": [
    "net = net.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c998137-0967-488f-a572-a5f5a6b86353",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.randn(16, 1, 576, 640)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "920aeeb2-088c-4ea0-84a2-a2532d4f697a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "119ab631-fb3a-47a3-afc2-0e66260ebe7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "xx, l = net(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ccdec29-3952-460d-95b4-820b03aa4997",
   "metadata": {},
   "outputs": [],
   "source": [
    "xx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a847084a-a65d-4072-ae1e-ae5d85a1664a",
   "metadata": {},
   "outputs": [],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b21480a-707b-41de-b75d-30fb467973a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "vq(x)[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cba1096d-8832-4955-88c9-a8650cf968cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "443a52d9-09f3-4e24-8a23-e0397a65f747",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78541477-6f02-42da-ad75-4a47bb043e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdedced3-e08b-4bec-822c-e5dcd521c6b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "list(Path(code_dir).glob(\"**/*.py\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79771541-c474-46a9-afdf-f74e736d6c16",
   "metadata": {},
   "outputs": [],
   "source": [
    "for path in glob.glob(os.path.join(code_dir, \"**/*.py\"), recursive=True):\n",
    "    print(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a79a2a20-56df-48b3-b964-22a0def52117",
   "metadata": {},
   "outputs": [],
   "source": [
    "e = Encoder(1, 64, 32, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a6fd004-6d7c-4a20-9ed4-508a73b329b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = Decoder(64, 1, 32, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82c18401-ea33-4ab6-ace4-03cb6e2e4435",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = e(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64f99b20-fa37-4614-b258-5870b7668959",
   "metadata": {},
   "outputs": [],
   "source": [
    "xh = d(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a81e7de-1203-4ab6-9562-37341e135daf",
   "metadata": {},
   "outputs": [],
   "source": [
    "xh.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "204d167b-dce0-4dd7-b0e1-88a53859fd28",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [2, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b77a6e8a-070d-46d3-9470-a5729eace57f",
   "metadata": {},
   "outputs": [],
   "source": [
    "a += [1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "741adac8-acc4-4715-afe9-07d3522cab62",
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49b894be-5947-4e06-b698-bb990bf2c64c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4371af97-1f3b-4c5e-9812-3fb97d07c1cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "576 // (2 * 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28224cc8-79e0-481f-b24c-85bd0ef69f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "16 // 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3a6146b-94b1-4618-a4e4-00f8e23ffdb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hydra import compose, initialize\n",
    "from omegaconf import OmegaConf\n",
    "from hydra.utils import instantiate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "764c8736-7d68-4261-a57d-face10ebbf42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# context initialization\n",
    "with initialize(config_path=\"../training/conf/\", job_name=\"test_app\"):\n",
    "    cfg = compose(config_name=\"config\", overrides=[\"+experiment=vqvae\"])\n",
    "    print(OmegaConf.to_yaml(cfg))\n",
    "    print(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1a9aa6b-6405-4ffe-b065-02340762476a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = instantiate(cfg.mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "969ba3be-d78f-4b1e-b522-ea8a42669e86",
   "metadata": {},
   "outputs": [],
   "source": [
    "network = instantiate(cfg.network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6147cd3e-0ad1-490f-917d-21be9bb8ce1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.rand(1, 1, 576, 640)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0ecea0c-abaf-4d5d-a13d-c085c1e4d282",
   "metadata": {},
   "outputs": [],
   "source": [
    "network.encode(x)[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7b9f249-7e5e-4f31-bbe1-cfd6d3701cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "t, l = network(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a9450d2-f45d-4823-adac-68a8ea05ed1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93b8c90f-788a-4095-aa7a-55b34f0ddaaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import functional as F\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9983788-2dae-4375-a821-a64cd1c68edf",
   "metadata": {},
   "outputs": [],
   "source": [
    "F.mse_loss(x, t) + l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29b128ca-80b7-481e-bb3c-44f109c7d292",
   "metadata": {},
   "outputs": [],
   "source": [
    "t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23c9d90c-042b-423e-ab85-18449e29ded4",
   "metadata": {},
   "outputs": [],
   "source": [
    "576 / 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "047ebc09-1c74-44a7-a314-1099f09722fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = torch.randint(0, 1006, (1, 451)).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87372dde-2b1a-432b-ab79-0b116124c724",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = torch.rand((1, 36 * 40, 128)).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf7ca9bf-cafa-4128-9db7-046c16933a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "network = network.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfceaa5f-9ad8-4d33-addb-c56e8da48356",
   "metadata": {},
   "outputs": [],
   "source": [
    "network.decode(z, t).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9105fbbb-4363-4d3e-a01e-bc519c3b9c3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder = decoder.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5797ec4-7a6a-46fd-8adc-265df44d0341",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder(z, t).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a23893a9-a0da-4327-a617-dc0c2011e5e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "OmegaConf.set_struct(cfg, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6fae1fa-492d-4648-80fd-1c0dac659b02",
   "metadata": {},
   "outputs": [],
   "source": [
    "datamodule = instantiate(cfg.datamodule, mapping=mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "514053ef-fcac-4f3c-a7c8-72c6927d6798",
   "metadata": {},
   "outputs": [],
   "source": [
    "datamodule.prepare_data()\n",
    "datamodule.setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bad950b-a197-4c60-ad89-903124659a98",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(datamodule.train_dataloader())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7db05cbd-48b3-43fa-a99a-353126311879",
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6e01c15-9a1b-4036-87ae-78716c592264",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = cfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dc475fc-31f4-487e-88c8-b0f445131f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = instantiate(cfg.criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5c8ed64-d98c-47b5-baf2-1ba57a6c882f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import hydra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5ff5b24-f804-402b-a8ab-f366443025ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "    model = hydra.utils.instantiate(\n",
    "        config.model,\n",
    "        mapping=mapping,\n",
    "        network=network,\n",
    "        loss_fn=loss_fn,\n",
    "        optimizer_config=config.optimizer,\n",
    "        lr_scheduler_config=config.lr_scheduler,\n",
    "        _recursive_=False,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99f8a39f-8b10-4f7d-8bff-52794fd48717",
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping.get_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af2c8cfa-0b45-4681-b671-0f97ace62516",
   "metadata": {},
   "outputs": [],
   "source": [
    "net = instantiate(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f0742ad-5e2f-42d5-83e7-6e46398b4f0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40be59bc-db79-4af1-9df4-e280f7a56481",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = torch.rand(4, 1, 576, 640)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5a8f10b-edf5-4a18-9747-f016db72c384",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = torch.randint(0, 1006, (4, 451))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19423ef1-3d98-4af3-8748-fdd3bb817300",
   "metadata": {},
   "outputs": [],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0712ee7e-4f66-4fb1-bc91-d8a127eb7ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "net = net.cuda()\n",
    "img = img.cuda()\n",
    "y = y.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "719154b4-47db-4c91-bae4-8c572c4a4536",
   "metadata": {},
   "outputs": [],
   "source": [
    "net(img, y).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcb7db0f-0afe-44eb-9bb7-b988fbead95a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31af8ee1-28d3-46b8-a847-6506d29bc45c",
   "metadata": {},
   "outputs": [],
   "source": [
    "summary(net, [(1, 576, 640), (451,)], device=\"cpu\", depth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d6d836f-d169-48b4-92e6-ca17179e6f85",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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