code: def _build_multi_dataloader(
      self, dataset_type: "str" = "train"
  ) -> MultiDataLoader:
      loader_args = {}
      for key, datamodule in self.datamodules.items():
          loader_args[key] = getattr(
              datamodule, f"{dataset_type}_dataloader"
          )()
          if not hasattr(loader_args[key], "dataset"):
              loader_args[key].dataset = getattr(
                  datamodule, f"{dataset_type}_dataset"
              )
      iteration_strategy = self._build_iteration_strategy(
          self.config, loader_args
      )
      loader = MultiDataLoader(loader_args, iteration_strategy)
      return loader
  
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 6 for calling the MultiDataModule obj's underlying member method code with example usage: obj._build_multi_dataloader(dataset_type) and returns: loader