code: def test_export_coreml_with_predict(self):
      import coremltools as _coremltools
  
      for test_number in range(len(self.models)):
          feature = self.feature
          model = self.models[test_number]
          sf = self.trains[test_number]
          if self.warm_start:
              prefix = "pretrained" + str(test_number)
          else:
              prefix = "scratch" + str(test_number)
          filename = tempfile.NamedTemporaryFile(
              prefix=prefix, suffix=".mlmodel"
          ).name
          model.export_coreml(filename)
          mlmodel = _coremltools.models.MLModel(filename)
          tc_preds = model.predict(sf)
  
          if test_number == 1:
              # stroke input
              sf[feature] = _tc.drawing_classifier.util.draw_strokes(
                  sf[self.feature]
              )
  
          for row_number in range(len(sf)):
              core_ml_preds = mlmodel.predict(
                  {"drawing": sf[feature][row_number]._to_pil_image()}
              )
              assert core_ml_preds[self.target] == tc_preds[row_number]
  
          if test_number == 1:
              sf = sf.remove_column(feature)
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import os
  import turicreate
  from turicreate.toolkits._main import ToolkitError
  import numpy
  import tempfile
  from array import array
  import sys
  from .None import util
  import unittest
label_desc: Use image node_id 26 for calling the DrawingClassifierTest obj's underlying member method code with example usage: obj.test_export_coreml_with_predict() without return types