code: def test_missing_indicator(
      failure_logger,
      int_dataset,
      missing_values,
      features,  # noqa: F811
  ):
      zero_filled, one_filled, nan_filled = int_dataset
      if missing_values == 0:
          X_np, X = zero_filled
      elif missing_values == 1:
          X_np, X = one_filled
      else:
          X_np, X = nan_filled
  
      indicator = cuMissingIndicator(
          missing_values=missing_values, features=features
      )
      ft_X = indicator.fit_transform(X)
      assert type(ft_X) == type(X)
      indicator.fit(X)
      t_X = indicator.transform(X)
      assert type(t_X) == type(X)
  
      indicator = skMissingIndicator(
          missing_values=missing_values, features=features
      )
      sk_ft_X = indicator.fit_transform(X_np)
      indicator.fit(X_np)
      sk_t_X = indicator.transform(X_np)
  
      assert_allclose(ft_X, sk_ft_X)
      assert_allclose(t_X, sk_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 29 for calling a global function with example usage: test_missing_indicator(failure_logger, int_dataset, missing_values, features) without return types