code: def __init__(
      self,
      num_features=32,
      length=800,
      num_classes=3,
      batch_size=10,
      n_clusters_per_class=1,
      n_informative=2,
  ):
      if not _SKLEARN_AVAILABLE:
          raise ImportError(str(_SKLEARN_AVAILABLE))
  
      from sklearn.datasets import make_classification
  
      data = make_classification(
          n_samples=length,
          n_features=num_features,
          n_classes=num_classes,
          n_clusters_per_class=n_clusters_per_class,
          n_informative=n_informative,
          random_state=42,
      )
      super().__init__(
          data,
          x_type=torch.float32,
          y_type=torch.long,
          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 ClassifDataModule object from inherited base classes: SklearnDataModule with example: obj = ClassifDataModule(num_features, length, num_classes, batch_size, n_clusters_per_class, n_informative)