code: def test_future_version(self):
      self.spec.specificationVersion = 10000
      filename = tempfile.mktemp(suffix=".mlmodel")
      save_spec(
          self.spec, filename, auto_set_specification_version=False
      )
      model = MLModel(filename)
      # this model should exist, but throw an exception when we try to use
      # predict because the engine doesn't support this model version
      self.assertIsNotNone(model)
      with self.assertRaises(Exception):
          try:
              model.predict({})
          except Exception as e:
              assert "Core ML model specification version" in str(e)
              raise
      self.spec.specificationVersion = 1
  
imports: import os
  import tempfile
  import unittest
  import numpy
  import PIL.Image
  import coremltools
  from coremltools import ComputeUnit
  from coremltools._deps import _HAS_TORCH
  from coremltools.converters.mil import Builder
  from coremltools.models import MLModel, datatypes
  from coremltools.models.neural_network import NeuralNetworkBuilder
  from coremltools.models.neural_network.utils import make_image_input, make_nn_classifier
  from coremltools.models.utils import _convert_neural_network_spec_weights_to_fp16, _is_macos, _macos_version, convert_double_to_float_multiarray_type, rename_feature, save_spec
  from coremltools.proto import Model_pb2
label_desc: Use image node_id 10 for calling the MLModelTest obj's underlying member method code with example usage: obj.test_future_version() without return types