code: def __init__(self, config: DictConfig):
      super().__init__()
      self.config = config
      self.batch_size = get_batch_size()
  
      self.dataset_list: List[str] = dataset_list_from_config(
          self.config
      )
      self.datamodules: Dict[
          str, pl.LightningDataModule
      ] = build_multiple_datamodules(
          self.dataset_list, self.config.dataset_config
      )
      self.train_loader: Optional[MultiDataLoader] = None
      self.val_loader: Optional[MultiDataLoader] = None
      self.test_loader: Optional[MultiDataLoader] = None
  
imports: import logging
  import warnings
  from typing import Dict, List, Optional
  import pytorch_lightning
  from mmf.common.sample import SampleList
  from mmf.common.test_reporter import TestReporter
  from mmf.datasets.iteration_strategies import IterationStrategy
  from mmf.datasets.multi_dataset_loader import MultiDataLoader
  from mmf.utils.build import build_iteration_strategy, build_multiple_datamodules, build_test_reporter
  from mmf.utils.dataset import dataset_list_from_config
  from mmf.utils.general import get_batch_size
  from omegaconf import DictConfig, OmegaConf
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 to create a new MultiDataModule object from inherited base classes: pl with example: obj = MultiDataModule(config)