code: def roundtrip_and_compare_mlmodel(mlmodel, input_dict):
      roundtripped_mlmodel = get_roundtrip_mlmodel(mlmodel)
      expected_outputs = mlmodel.predict(input_dict)
      compare_backend(
          roundtripped_mlmodel, input_dict, expected_outputs
      )
  
imports: import numpy
  import pytest
  import coremltools
  from coremltools import ComputeUnit
  from coremltools._deps import _HAS_TF_2, _HAS_TORCH
  from coremltools.converters._converters_entry import _get_metadata_from_mlmodel
  from coremltools.converters.mil import Builder
  from coremltools.converters.mil.converter import mil_convert
  from coremltools.converters.mil.frontend.milproto.load import load
  from coremltools.converters.mil.frontend.tensorflow.test.test_ops import TestTensorArray
  from coremltools.converters.mil.frontend.tensorflow.test.testing_utils import run_compare_tf
  from coremltools.converters.mil.mil.ops.tests.testing_utils import compare_backend
  from coremltools.converters.mil.testing_utils import get_op_names_in_program, get_op_types_in_program
label_desc: Use image node_id 3 for calling a global function with example usage: roundtrip_and_compare_mlmodel(mlmodel, input_dict) without return types