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 = LightningMultiDataLoader(loader_args, iteration_strategy)
      return loader
  
file_path: mmf/mmf/datasets/lightning_multi_datamodule.py
imports: import logging
  from mmf.datasets.lightning_multi_dataset_loader import LightningMultiDataLoader
  from mmf.datasets.multi_datamodule import MultiDataModule
  from mmf.datasets.multi_dataset_loader import MultiDataLoader
  from omegaconf import DictConfig
label_desc: Use image node_id 2 for calling the LightningMultiDataModule obj's underlying member method code with example usage: obj._build_multi_dataloader(dataset_type) and returns: loader