code: def test_data():
      X, _ = make_classification(
          n_samples=20,
          n_features=100,
          n_informative=5,
          n_classes=2,
          random_state=0,
      )
      X2, _ = make_classification(
          n_samples=10,
          n_features=100,
          n_informative=5,
          n_classes=2,
          random_state=0,
      )
  
      # Sparsify datasets.
      X[X < 0.3] = 0
  
      X_csr = sp.csr_matrix(X)
      X_csc = sp.csc_matrix(X)
  
      rs = check_random_state(0)
      cds = ContiguousDataset(X)
      fds = FortranDataset(np.asfortranarray(X))
      csr_ds = CSRDataset(X_csr)
      csc_ds = CSCDataset(X_csc)
  
      return {
          "X": X,
          "X_csr": X_csr,
          "X_csc": X_csc,
          "contiguous_dataset": cds,
          "fortran_dataset": fds,
          "dataset_csr": csr_ds,
          "dataset_csc": csc_ds,
      }
  
file_path: lightning/lightning/impl/tests/test_dataset.py
imports: import pickle
  import numpy
  import pytest
  import scipy.sparse
  from sklearn.datasets import make_classification
  from sklearn.utils import check_random_state
  from lightning.impl.dataset_fast import ContiguousDataset
  from lightning.impl.dataset_fast import FortranDataset
  from lightning.impl.dataset_fast import CSRDataset
  from lightning.impl.dataset_fast import CSCDataset
label_desc: Use image node_id 1 for calling a global function with example usage: test_data() and returns: dict