code: def test_boston_OHE_plus_normalizer(self):
      data = load_boston()
  
      pl = Pipeline(
          [
              (
                  "OHE",
                  OneHotEncoder(categorical_features=[8], sparse=False),
              ),
              ("Scaler", StandardScaler()),
          ]
      )
  
      pl.fit(data.data, data.target)
  
      # Convert the model
      spec = convert(pl, data.feature_names, "out")
  
      if _is_macos() and _macos_version() >= (10, 13):
          input_data = [
              dict(zip(data.feature_names, row)) for row in data.data
          ]
          output_data = [
              {"out": row} for row in pl.transform(data.data)
          ]
  
          result = evaluate_transformer(spec, input_data, output_data)
          assert result["num_errors"] == 0
  
file_path: turicreate/src/external/coremltools_wrap/coremltools/coremltools/test/sklearn_tests/test_composite_pipelines.py
imports: import unittest
  import os
  import pandas
  import itertools
  import numpy
  from coremltools._deps import _HAS_SKLEARN
  from coremltools.models.utils import evaluate_transformer
  from coremltools.models.utils import evaluate_regressor
  from coremltools.models.utils import _macos_version, _is_macos
  from coremltools.converters.sklearn import convert
label_desc: Use image node_id 1 for calling the GradientBoostingRegressorBostonHousingScikitNumericTest obj's underlying member method code with example usage: obj.test_boston_OHE_plus_normalizer() without return types