code: def __init__(
      self,
      batch_size,
      train=None,
      validation=None,
      test=None,
      predict=None,
      wrap=False,
      num_workers=None,
      shuffle_test_loader=False,
      use_worker_init_fn=False,
      shuffle_val_dataloader=False,
  ):
      super().__init__()
      self.batch_size = batch_size
      self.dataset_configs = dict()
      self.num_workers = (
          num_workers if num_workers is not None else batch_size * 2
      )
      self.use_worker_init_fn = use_worker_init_fn
      if train is not None:
          self.dataset_configs["train"] = train
          self.train_dataloader = self._train_dataloader
      if validation is not None:
          self.dataset_configs["validation"] = validation
          self.val_dataloader = partial(
              self._val_dataloader, shuffle=shuffle_val_dataloader
          )
      if test is not None:
          self.dataset_configs["test"] = test
          self.test_dataloader = partial(
              self._test_dataloader, shuffle=shuffle_test_loader
          )
      if predict is not None:
          self.dataset_configs["predict"] = predict
          self.predict_dataloader = self._predict_dataloader
      self.wrap = wrap
  
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 1 to create a new DataModuleFromConfig object from inherited base classes: pl with example: obj = DataModuleFromConfig(batch_size, train, validation, test, predict, wrap, num_workers, shuffle_test_loader, use_worker_init_fn, shuffle_val_dataloader)