code: def test_poly_features(
      failure_logger,
      clf_dataset,
      degree,  # noqa: F811
      interaction_only,
      include_bias,
      order,
  ):
      X_np, X = clf_dataset
  
      polyfeatures = cuPolynomialFeatures(
          degree=degree,
          order=order,
          interaction_only=interaction_only,
          include_bias=include_bias,
      )
      t_X = polyfeatures.fit_transform(X)
      assert type(X) == type(t_X)
      cu_feature_names = polyfeatures.get_feature_names()
  
      if isinstance(t_X, np.ndarray):
          if order == "C":
              assert t_X.flags["C_CONTIGUOUS"]
          elif order == "F":
              assert t_X.flags["F_CONTIGUOUS"]
  
      polyfeatures = skPolynomialFeatures(
          degree=degree,
          order=order,
          interaction_only=interaction_only,
          include_bias=include_bias,
      )
      sk_t_X = polyfeatures.fit_transform(X_np)
      if sklearn.__version__ <= "1.0":
          sk_feature_names = polyfeatures.get_feature_names()
  
      assert_allclose(t_X, sk_t_X, rtol=0.1, atol=0.1)
      if sklearn.__version__ <= "1.0":
          assert sk_feature_names == cu_feature_names
  
imports: from cuml.internals.safe_imports import gpu_only_import
  import pytest
  import sklearn
  from cuml.preprocessing import Binarizer, FunctionTransformer, KBinsDiscretizer, KernelCenterer, MaxAbsScaler, MinMaxScaler, MissingIndicator, Normalizer, PolynomialFeatures, PowerTransformer, QuantileTransformer, RobustScaler, SimpleImputer, StandardScaler
  from cuml.preprocessing import add_dummy_feature, binarize, maxabs_scale, minmax_scale, normalize, power_transform, quantile_transform, robust_scale, scale
  from sklearn.preprocessing import Binarizer, FunctionTransformer, KBinsDiscretizer, KernelCenterer, MaxAbsScaler, MinMaxScaler, Normalizer, PolynomialFeatures, PowerTransformer, QuantileTransformer, RobustScaler, StandardScaler
  from sklearn.preprocessing import add_dummy_feature, binarize, maxabs_scale, minmax_scale, normalize, power_transform, quantile_transform, robust_scale, scale
  from sklearn.impute import MissingIndicator, SimpleImputer
  from cuml.testing.test_preproc_utils import clf_dataset, int_dataset, blobs_dataset, nan_filled_positive, sparse_nan_filled_positive, sparse_clf_dataset, sparse_blobs_dataset, sparse_int_dataset, sparse_imputer_dataset, sparse_dataset_with_coo
  from cuml.testing.test_preproc_utils import assert_allclose
  from cuml.metrics import pairwise_kernels
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 16 for calling a global function with example usage: test_poly_features(failure_logger, clf_dataset, degree, interaction_only, include_bias, order) without return types