code: def __init__(
      self, batch_size: int = 8, dataset_size: int = 256
  ) -> None:
      super().__init__()
      self.batch_size = batch_size
      self.train_data = torch.randn(dataset_size, 32)
      self.val_data = torch.randn(dataset_size, 32)
      self.test_data = torch.randn(dataset_size, 32)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torchmetrics import Accuracy
  from ray import train
  from ray.train.lightning._lightning_utils import import_lightning
label_desc: Use image node_id 1 to create a new DummyDataModule object from inherited base classes: pl with example: obj = DummyDataModule(batch_size, dataset_size)