code: def __init__(self, num_features=16, length=800, batch_size=10):
      if not _SKLEARN_AVAILABLE:
          raise ImportError(str(_SKLEARN_AVAILABLE))
  
      from sklearn.datasets import make_regression
  
      x, y = make_regression(
          n_samples=length, n_features=num_features, random_state=42
      )
      y = [[v] for v in y]
      super().__init__(
          (x, y),
          x_type=torch.float32,
          y_type=torch.float32,
          batch_size=batch_size,
      )
  
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 RegressDataModule object from inherited base classes: SklearnDataModule with example: obj = RegressDataModule(num_features, length, batch_size)