class_docstr: Unit test class for testing scikit-learn converter.
code: def test_boston_OHE_pipeline(self):
      data = load_boston()
  
      for categorical_features in [[3], [8], [3, 8], [8, 3]]:
          # Put it in a pipeline so that we can test whether the output dimension
          # handling is correct.
  
          model = Pipeline(
              [
                  (
                      "OHE",
                      OneHotEncoder(
                          categorical_features=categorical_features
                      ),
                  ),
                  ("Normalizer", Normalizer()),
              ]
          )
  
          model.fit(data.data.copy(), data.target)
  
          # Convert the model
          spec = sklearn.convert(
              model, data.feature_names, "out"
          ).get_spec()
  
          input_data = [
              dict(zip(data.feature_names, row)) for row in data.data
          ]
          output_data = [
              {"out": row} for row in model.transform(data.data.copy())
          ]
  
          result = evaluate_transformer(spec, input_data, output_data)
  
          assert result["num_errors"] == 0
  
file_path: coremltools/coremltools/test/sklearn_tests/test_one_hot_encoder.py
imports: import unittest
  from copy import copy
  from distutils.version import StrictVersion
  import numpy
  from coremltools._deps import _HAS_SKLEARN, _SKLEARN_VERSION
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_transformer
label_desc: Use image node_id 6 for calling the OneHotEncoderScikitTest obj's underlying member method code with example usage: obj.test_boston_OHE_pipeline() without return types