class_docstr: Unit test class for testing scikit-learn converter.
code: def test_boston(self):
      from sklearn.datasets import load_boston
  
      scikit_data = load_boston()
      scikit_model = StandardScaler().fit(scikit_data.data)
  
      spec = converter.convert(
          scikit_model, scikit_data.feature_names, "out"
      ).get_spec()
  
      input_data = [
          dict(zip(scikit_data.feature_names, row))
          for row in scikit_data.data
      ]
  
      output_data = [
          {"out": row}
          for row in scikit_model.transform(scikit_data.data)
      ]
  
      metrics = evaluate_transformer(spec, input_data, output_data)
  
      assert metrics["num_errors"] == 0
  
file_path: coremltools/coremltools/test/sklearn_tests/test_standard_scalar.py
imports: import unittest
  import numpy
  from coremltools._deps import _HAS_SKLEARN
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_transformer
label_desc: Use image node_id 2 for calling the StandardScalerTestCase obj's underlying member method code with example usage: obj.test_boston() without return types