code: def test_column_transformer(self):
      # see issue #169
      for encoder_name in set(encoders.__all__) - {
          "HashingEncoder"
      }:  # HashingEncoder does not accept handle_missing parameter
          with self.subTest(encoder_name=encoder_name):
              # we can only test one data type at once. Here, we test string columns.
              tested_columns = [
                  "unique_str",
                  "invariant",
                  "underscore",
                  "none",
                  "extra",
              ]
  
              # ColumnTransformer instantiates the encoder twice -> we have to make sure the encoder settings are correctly passed
              ct = ColumnTransformer(
                  [
                      (
                          "dummy_encoder_name",
                          getattr(encoders, encoder_name)(
                              handle_missing="return_nan"
                          ),
                          tested_columns,
                      )
                  ]
              )
              obtained = ct.fit_transform(X, y)
  
              # the old-school approach
              enc = getattr(encoders, encoder_name)(
                  handle_missing="return_nan", return_df=False
              )
              expected = enc.fit_transform(X[tested_columns], y)
  
              np.testing.assert_array_equal(obtained, expected)
  
imports: import warnings
  from datetime import timedelta
  import numpy
  from numpy.testing import assert_array_equal
  import pandas
  import sklearn
  import tests.helpers
  from sklearn.utils.estimator_checks import check_transformer_general, check_transformers_unfitted, check_n_features_in
  from sklearn.compose import ColumnTransformer
  from unittest import TestCase
  from copy import deepcopy
  import category_encoders
label_desc: Use image node_id 34 for calling the TestEncoders obj's underlying member method code with example usage: obj.test_column_transformer() without return types