code: def build_multiple_datamodules(
      dataset_list: List[str], all_dataset_config: DictConfig
  ) -> Dict[str, pl.LightningDataModule]:
      datamodules: Dict[str, pl.LightningDataModule] = {}
      for dataset in dataset_list:
          datamodule_instance = build_datamodule(dataset)
          if dataset in all_dataset_config:
              dataset_config = all_dataset_config[dataset]
          else:
              warnings.warn(
                  f"Dataset {dataset} is missing from dataset_config"
                  + " in config. Proceeding with empty config."
              )
              dataset_config = OmegaConf.create()
  
          if is_main():
              datamodule_instance.prepare_data(dataset_config)
  
          synchronize()
          datamodule_instance.setup(config=dataset_config)
          if hasattr(datamodule_instance, "update_registry_for_model"):
              datamodule_instance.update_registry_for_model(
                  dataset_config
              )
          datamodules[dataset] = datamodule_instance
      return datamodules
  
imports: import logging
  import os
  import warnings
  from enum import Enum
  from typing import Any, Dict, List, Optional, Tuple, Union
  import mmf
  import pytorch_lightning
  import torch
  from mmf.common.meter import Meter
  from mmf.common.registry import registry
  from mmf.datasets.iteration_strategies import ConstantIterationStrategy, IterationStrategy, SizeProportionalIterationStrategy
  from mmf.datasets.processors.processors import Processor
  from mmf.utils.configuration import Configuration, get_global_config
  from mmf.utils.distributed import is_dist_initialized, is_main, is_xla, synchronize
  from mmf.utils.general import get_optimizer_parameters
  from omegaconf import DictConfig, OmegaConf
  from packaging import version
label_desc: Use image node_id 8 for calling a global function with example usage: build_multiple_datamodules(dataset_list, all_dataset_config) and returns: datamodules