code: def __init__(self, config, num_data):
      super().__init__(config)
      batch_size = config.training.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": {"numbers": 0},
          }
      )
      self._num_data = num_data
      self.batch_size = batch_size
      self.config = config
      self.dataset_list = []
      dataset_builder = MMFDatasetBuilder(
          "numbers",
          functools.partial(
              NumbersDataset, num_examples=num_data, always_one=True
          ),
      )
      dataset_builder.train_dataloader = self._get_dataloader_train
      dataset_builder.val_dataloader = self._get_dataloader_val
      dataset_builder.test_dataloader = self._get_dataloader_test
      self.datamodules = {"numbers": dataset_builder}
  
imports: import functools
  from unittest.mock import MagicMock
  import torch
  from mmf.common.registry import registry
  from mmf.common.sample import SampleList
  from mmf.datasets.lightning_multi_datamodule import LightningMultiDataModule
  from mmf.datasets.mmf_dataset_builder import MMFDatasetBuilder
  from mmf.datasets.multi_datamodule import MultiDataModule
  from mmf.trainers.mmf_trainer import MMFTrainer
  from omegaconf import OmegaConf
  from tests.test_utils import NumbersDataset
label_desc: Use image node_id 1 to create a new LightningMultiDataModuleNumbersTestObject object from inherited base classes: LightningMultiDataModule with example: obj = LightningMultiDataModuleNumbersTestObject(config, num_data)