docstr: Train/validation loaders.
docstr_tok: ["Train\/validation","loaders","."]
code: def __dataloader(self, train: bool):
      """Train/validation loaders."""
      if train:
          dataset = self.create_dataset(
              self.data_path.joinpath("train"), self.train_transform
          )
      else:
          dataset = self.create_dataset(
              self.data_path.joinpath("validation"),
              self.valid_transform,
          )
      return DataLoader(
          dataset=dataset,
          batch_size=self._batch_size,
          num_workers=self._num_workers,
          shuffle=train,
      )
  
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 8 for calling the CatDogImageDataModule obj's underlying member method code with example usage: obj.__dataloader(train) and returns: DataLoader