code: def _train_dataloader(self):
      is_iterable_dataset = isinstance(
          self.datasets["train"], Txt2ImgIterableBaseDataset
      )
      if is_iterable_dataset or self.use_worker_init_fn:
          init_fn = worker_init_fn
      else:
          init_fn = None
      return DataLoader(
          self.datasets["train"],
          batch_size=self.batch_size,
          num_workers=self.num_workers,
          shuffle=False if is_iterable_dataset else True,
          worker_init_fn=init_fn,
      )
  
imports: import argparse, os, sys, datetime, glob, importlib, csv
  import numpy
  import time
  import torch
  import torchvision
  import pytorch_lightning
  from packaging import version
  from omegaconf import OmegaConf
  from torch.utils.data import random_split, DataLoader, Dataset, Subset
  from functools import partial
  from PIL import Image
  from pytorch_lightning import seed_everything
  from pytorch_lightning.trainer import Trainer
  from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor
  from pytorch_lightning.utilities.distributed import rank_zero_only
  from pytorch_lightning.utilities import rank_zero_info
  from ldm.data.base import Txt2ImgIterableBaseDataset
  from ldm.util import instantiate_from_config
label_desc: Use image node_id 4 for calling the DataModuleFromConfig obj's underlying member method code with example usage: obj._train_dataloader() and returns: DataLoader