docstr: CatDogImageDataModule.
  
  Args:
      dl_path: root directory where to download the data
      num_workers: number of CPU workers
      batch_size: number of sample in a batch
docstr_tok: ["CatDogImageDataModule",".","Args",":","dl_path",":","root","directory","where","to","download","the","data","num_workers",":","number","of","CPU","workers","batch_size",":","number","of","sample","in","a","batch"]
code: def __init__(
      self,
      dl_path: Union[str, Path] = "data",
      num_workers: int = 0,
      batch_size: int = 8,
  ):
      """CatDogImageDataModule.
  
      Args:
          dl_path: root directory where to download the data
          num_workers: number of CPU workers
          batch_size: number of sample in a batch
  
      """
      super().__init__()
  
      self._dl_path = dl_path
      self._num_workers = num_workers
      self._batch_size = batch_size
  
imports: import logging
  from pathlib import Path
  from typing import Union
  import torch
  import torch.nn.functional
  from lightning.pytorch import LightningDataModule, LightningModule, cli_lightning_logo
  from lightning.pytorch.callbacks.finetuning import BaseFinetuning
  from lightning.pytorch.cli import LightningCLI
  from lightning.pytorch.utilities import rank_zero_info
  from lightning.pytorch.utilities.model_helpers import get_torchvision_model
  from torch import nn, optim
  from torch.optim.lr_scheduler import MultiStepLR
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from torchmetrics import Accuracy
  from torchvision import transforms
  from torchvision.datasets import ImageFolder
  from torchvision.datasets.utils import download_and_extract_archive
label_desc: Use image node_id 1 to create a new CatDogImageDataModule object from inherited base classes: LightningDataModule with example: obj = CatDogImageDataModule(dl_path, num_workers, batch_size)