code: def test_export_coreml_predict(self):
      import coremltools
  
      filename = tempfile.NamedTemporaryFile(suffix=".mlmodel").name
      self.model.export_coreml(filename)
  
      coreml_model = coremltools.models.MLModel(filename)
      if self.feature == "awesome_image":
          # tc.image_classifier.create(...) was not called with deep features
          img = data[0:1][self.feature][0]
          img_fixed = tc.image_analysis.resize(
              img, *reversed(self.input_image_shape)
          )
          from PIL import Image
  
          pil_img = Image.fromarray(img_fixed.pixel_data)
  
          if _mac_ver() >= (10, 13):
              classes = self.model.classifier.classes
              ret = coreml_model.predict({self.feature: pil_img})
              coreml_values = [
                  ret[self.target + "Probability"][l] for l in classes
              ]
  
              self.assertListAlmostEquals(
                  coreml_values,
                  list(
                      self.model.predict(
                          img_fixed, output_type="probability_vector"
                      )
                  ),
                  self.tolerance,
              )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import os
  import platform
  import sys
  import tempfile
  import unittest
  from turicreate.toolkits._main import ToolkitError
  from turicreate.toolkits._internal_utils import _mac_ver, _raise_error_if_not_sframe, _raise_error_if_not_sarray
  from turicreate.toolkits.image_analysis.image_analysis import MODEL_TO_FEATURE_SIZE_MAPPING, get_deep_features
  from .None import util
  import numpy
  import turicreate
label_desc: Use image node_id 13 for calling the ImageClassifierTest obj's underlying member method code with example usage: obj.test_export_coreml_predict() without return types