code: def test_convert_image_input_preprocess(self):  # type: () -> None
      bias = np.array([100, 90, 80])
      coreml_model = convert(
          self.onnx_model,
          image_input_names=self.input_names,
          preprocessing_args={
              "is_bgr": True,
              "blue_bias": bias[0],
              "green_bias": bias[1],
              "red_bias": bias[2],
          },
      )
  
      if _IS_MACOS:
          output = coreml_model.predict(
              {self.input_names[0]: self.img}
          )[self.output_names[0]]
  
          expected_output = self.img_arr[:, :, ::-1].transpose(
              (2, 0, 1)
          )
          expected_output[0] = expected_output[0] + bias[0]
          expected_output[1] = expected_output[1] + bias[1]
          expected_output[2] = expected_output[2] + bias[2]
          npt.assert_equal(output.flatten(), expected_output.flatten())
  
file_path: turicreate/src/external/coremltools_wrap/coremltools/coremltools/converters/onnx/_tests/test_convert.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from coremltools._deps import _HAS_ONNX, MSG_ONNX_NOT_FOUND, _IS_MACOS
  import unittest
  import numpy
  import numpy.testing
  import numpy.random
  from PIL import Image
label_desc: Use image node_id 4 for calling the ConvertTest obj's underlying member method code with example usage: obj.test_convert_image_input_preprocess() without return types