code: def convert_and_compare(
      input_data,
      model_spec,
      expected_results=None,
      atol=1e-5,
      backend="nn_proto",
  ):
      """
      If expected results is not set, it will by default
      be set to the flattened output of the torch model.
      """
      if isinstance(model_spec, _string_types):
          torch_model = torch.jit.load(model_spec)
      else:
          torch_model = model_spec
  
      if not isinstance(input_data, (list, tuple)):
          input_data = [input_data]
  
      if not expected_results:
          expected_results = torch_model(*input_data)
      expected_results = flatten_and_detach_torch_results(
          expected_results
      )
      mlmodel = convert_to_mlmodel(
          model_spec, input_data, backend=backend
      )
      coreml_inputs = convert_to_coreml_inputs(
          mlmodel.input_description, input_data
      )
      if _IS_MACOS:
          coreml_results = mlmodel.predict(coreml_inputs)
          sorted_coreml_results = [
              coreml_results[key]
              for key in sorted(coreml_results.keys())
          ]
  
          for torch_result, coreml_result in zip(
              expected_results, sorted_coreml_results
          ):
              np.testing.assert_equal(
                  coreml_result.shape, torch_result.shape
              )
              np.testing.assert_allclose(
                  coreml_result, torch_result, atol=atol
              )
  
imports: import numpy
  import torch
  import torch.nn
  from six import string_types
  from coremltools import TensorType
  from coremltools.converters.mil.testing_reqs import _converter
  from coremltools.models import MLModel
  from coremltools._deps import _IS_MACOS
label_desc: Use image node_id 8 for calling a global function with example usage: convert_and_compare(input_data, model_spec, expected_results, atol, backend) without return types