code: def __init__(self, batch_size):
      self.batch_size = batch_size
      config = OmegaConf.create(
          {
              "use_features": True,
              "annotations": {
                  "train": "not_a_real_annotations_dataset",
                  "val": "not_a_real_annotations_dataset",
              },
              "features": {
                  "train": "not_a_real_features_dataset",
                  "val": "not_a_real_features_dataset",
              },
              "dataset_config": {"simple": 0},
          }
      )
      self.config = config
      self.dataset_list = []
      dataset_builder = MMFDatasetBuilder(
          "simple",
          functools.partial(SimpleMMFDataset, num_examples=100),
      )
      dataset_builder.train_dataloader = self._get_dataloader
      dataset_builder.val_dataloader = self._get_dataloader
      dataset_builder.test_dataloader = self._get_dataloader
      self.datamodules = {"simple": dataset_builder}
  
file_path: mmf/tests/datasets/test_multi_datamodule.py
imports: import functools
  import torch
  from mmf.datasets.lightning_multi_datamodule import LightningMultiDataModule
  from mmf.datasets.mmf_dataset_builder import MMFDatasetBuilder
  from mmf.datasets.multi_datamodule import MultiDataModule
  from omegaconf import OmegaConf
  from tests.datasets.test_mmf_dataset_builder import SimpleMMFDataset
label_desc: Use image node_id 1 to create a new LightningDataModuleTestObject object from inherited base classes: LightningMultiDataModule with example: obj = LightningDataModuleTestObject(batch_size)