code: def test_quantile_transformer(
      failure_logger,
      nan_filled_positive,  # noqa: F811
      n_quantiles,
      output_distribution,
      ignore_implicit_zeros,
      subsample,
  ):
      X_np, X = nan_filled_positive
  
      transformer = cuQuantileTransformer(
          n_quantiles=n_quantiles,
          output_distribution=output_distribution,
          ignore_implicit_zeros=ignore_implicit_zeros,
          subsample=subsample,
          random_state=42,
          copy=True,
      )
      t_X = transformer.fit_transform(X)
      assert type(t_X) == type(X)
      r_X = transformer.inverse_transform(t_X)
      assert type(r_X) == type(t_X)
  
      quantiles_ = transformer.quantiles_
      references_ = transformer.references_
  
      transformer = skQuantileTransformer(
          n_quantiles=n_quantiles,
          output_distribution=output_distribution,
          ignore_implicit_zeros=ignore_implicit_zeros,
          subsample=subsample,
          random_state=42,
          copy=True,
      )
      sk_t_X = transformer.fit_transform(X_np)
      sk_r_X = transformer.inverse_transform(sk_t_X)
  
      sk_quantiles_ = transformer.quantiles_
      sk_references_ = transformer.references_
  
      assert_allclose(quantiles_, sk_quantiles_)
      assert_allclose(references_, sk_references_)
  
      assert_allclose(t_X, sk_t_X)
      assert_allclose(r_X, sk_r_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 33 for calling a global function with example usage: test_quantile_transformer(failure_logger, nan_filled_positive, n_quantiles, output_distribution, ignore_implicit_zeros, subsample) without return types