code: def __init__(
      self,
      train_dataset: DictConfig = None,
      train_batch_size: int = 1,
      train_shuffle: bool = False,
      val_dataset: DictConfig = None,
      val_batch_size: int = 1,
      val_shuffle: bool = False,
      test_dataset: DictConfig = None,
      test_batch_size: int = 1,
      test_shuffle: bool = False,
  ):
      super().__init__()
  
      self.train_dataset = train_dataset
      self.train_batch_size = train_batch_size
      self.train_shuffle = train_shuffle
      self.val_dataset = val_dataset
      self.val_batch_size = val_batch_size
      self.val_shuffle = val_shuffle
      self.test_dataset = test_dataset
      self.test_batch_size = test_batch_size
      self.test_shuffle = test_shuffle
  
file_path: NeMo/examples/multimodal/x_to_nerf/data.py
imports: import pytorch_lightning
  from omegaconf.omegaconf import DictConfig
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 to create a new AggregatorDataModule object from inherited base classes: pl with example: obj = AggregatorDataModule(train_dataset, train_batch_size, train_shuffle, val_dataset, val_batch_size, val_shuffle, test_dataset, test_batch_size, test_shuffle)