code: def test_sklearn_cross_validation_iterators(
      data_args, cross_validation_method
  ):
      """Test if sklearn cross-validation iterators can handle sktime panel data."""
      fit_args = make_classification_problem(**data_args)
      groups = [1, 1, 2, 2, 3, 3, 4]
  
      for train, test in cross_validation_method.split(
          *fit_args, groups=groups
      ):
          assert isinstance(train, np.ndarray) and isinstance(
              test, np.ndarray
          )
  
file_path: sktime/sktime/classification/tests/test_sklearn_compatibility.py
imports: import numpy
  import pytest
  from sklearn.calibration import CalibratedClassifierCV
  from sklearn.ensemble import VotingClassifier
  from sklearn.experimental import enable_halving_search_cv
  from sklearn.model_selection import GridSearchCV, GroupKFold, GroupShuffleSplit, KFold, LeaveOneOut, LeavePGroupsOut, LeavePOut, RandomizedSearchCV, RepeatedKFold, ShuffleSplit, StratifiedKFold, StratifiedShuffleSplit, TimeSeriesSplit, cross_val_score
  from sklearn.pipeline import Pipeline
  from sktime.classification.interval_based import CanonicalIntervalForest
  from sktime.transformations.panel.pca import PCATransformer
  from sktime.utils._testing.panel import _make_panel_X, make_classification_problem
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 2 for calling a global function with example usage: test_sklearn_cross_validation_iterators(data_args, cross_validation_method) without return types