code: def __init__(
      self, sklearn_dataset, x_type, y_type, batch_size: int = 10
  ):
      if not _SKLEARN_AVAILABLE:
          raise ImportError(str(_SKLEARN_AVAILABLE))
  
      super().__init__()
      self.batch_size = batch_size
      self._x, self._y = sklearn_dataset
      self._split_data()
      self._x_type = x_type
      self._y_type = y_type
  
imports: import torch
  from lightning.pytorch.core.datamodule import LightningDataModule
  from lightning_utilities.core.imports import RequirementCache
  from torch.utils.data import DataLoader
  from tests_pytorch.helpers.datasets import MNIST, SklearnDataset, TrialMNIST
label_desc: Use image node_id 1 to create a new SklearnDataModule object from inherited base classes: LightningDataModule with example: obj = SklearnDataModule(sklearn_dataset, x_type, y_type, batch_size)