code: def test_default_configuration_sparse_data(self):
      transformations = []
  
      self.X_train[~np.isfinite(self.X_train)] = 0
      self.X_train = sparse.csc_matrix(self.X_train)
  
      for i in range(2):
          configuration_space = (
              OneHotEncoder.get_hyperparameter_search_space()
          )
          default_config = (
              configuration_space.get_default_configuration()
          )
  
          preprocessor = OneHotEncoder(random_state=1, **default_config)
  
          transformer = preprocessor.fit(self.X_train.copy())
          Xt = transformer.transform(self.X_train.copy())
          transformations.append(Xt)
          if len(transformations) > 1:
              self.assertEqual(
                  (
                      transformations[-1] != transformations[-2]
                  ).count_nonzero(),
                  0,
              )
  
file_path: auto-sklearn/test/test_pipeline/components/data_preprocessing/test_one_hot_encoding.py
imports: import numpy
  from scipy import sparse
  from autosklearn.pipeline.components.data_preprocessing.categorical_encoding.no_encoding import NoEncoding
  from autosklearn.pipeline.components.data_preprocessing.categorical_encoding.one_hot_encoding import OneHotEncoder
  from autosklearn.pipeline.util import _test_preprocessing
  import unittest
label_desc: Use image node_id 5 for calling the OneHotEncoderTest obj's underlying member method code with example usage: obj.test_default_configuration_sparse_data() without return types