code: def test_kernel_centerer():
      X = np.array(
          [[1.0, -2.0, 2.0], [-2.0, 1.0, 3.0], [4.0, 1.0, -2.0]]
      )
      K = pairwise_kernels(X, metric="linear")
  
      model = cuKernelCenterer()
      model.fit(K)
      t_X = model.transform(K, copy=True)
      assert type(t_X) == type(X)
  
      model = skKernelCenterer()
      sk_t_X = model.fit_transform(K)
  
      assert_allclose(sk_t_X, t_X)
  
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 38 for calling a global function with example usage: test_kernel_centerer() without return types