code: def convert_and_compare(
      input_data,
      model_spec,
      expected_results=None,
      atol=1e-4,
      rtol=1e-05,
      backend=("neuralnetwork", "fp32"),
      converter_input_type=None,
      compute_unit=ct.ComputeUnit.CPU_ONLY,
      minimum_deployment_target=None,
  ):
      """
      If expected results is not set, it will by default
      be set to the flattened output of the torch model.
  
      Inputs:
  
      - input_data: torch.tensor or list[torch.tensor]
      """
      if isinstance(model_spec, str):
          torch_model = torch.jit.load(model_spec)
      else:
          torch_model = model_spec
  
      if not isinstance(input_data, (list, tuple)):
          input_data = [input_data]
  
      if expected_results is None:
          torch_input = _copy_input_data(input_data)
          expected_results = torch_model(*torch_input)
      expected_results = flatten_and_detach_torch_results(
          expected_results
      )
      mlmodel = convert_to_mlmodel(
          model_spec,
          input_data,
          backend=backend,
          converter_input_type=converter_input_type,
          compute_unit=compute_unit,
          minimum_deployment_target=minimum_deployment_target,
      )
  
      coreml_inputs = convert_to_coreml_inputs(
          mlmodel.input_description, input_data
      )
  
      if not _IS_MACOS or (
          mlmodel.is_package
          and coremltoolsutils._macos_version() < (12, 0)
      ):
          return model_spec, mlmodel, coreml_inputs, None
  
      _, dtype = backend
      if mlmodel.compute_unit != ct.ComputeUnit.CPU_ONLY or (
          dtype == "fp16"
      ):
          atol = max(atol * 100.0, 5e-1)
          rtol = max(rtol * 100.0, 5e-2)
  
      if not coremltoolsutils._has_custom_layer(mlmodel._spec):
          coreml_preds = mlmodel.predict(coreml_inputs)
          coreml_outputs = mlmodel._spec.description.output
          coreml_results = [
              coreml_preds[output.name] for output in coreml_outputs
          ]
          for torch_result, coreml_result in zip(
              expected_results, coreml_results
          ):
              if torch_result.shape == ():
                  torch_result = np.array([torch_result])
              np.testing.assert_equal(
                  coreml_result.shape, torch_result.shape
              )
              np.testing.assert_allclose(
                  coreml_result, torch_result, atol=atol, rtol=rtol
              )
      return model_spec, mlmodel, coreml_inputs, coreml_preds
  
imports: import numpy
  import pytest
  import torch
  import torch.nn
  import coremltools
  import coremltools.models.utils
  from coremltools import RangeDim, TensorType
  from coremltools._deps import _HAS_EXECUTORCH, _HAS_TORCH_EXPORT_API, _IS_MACOS
  from coremltools.converters.mil.mil.types.type_mapping import nptype_from_builtin
  from coremltools.converters.mil.testing_utils import ct_convert, validate_minimum_deployment_target
  from ..ops import TorchFrontend
  from ..torchscript_utils import torch_to_mil_types
label_desc: Use image node_id 9 for calling a global function with example usage: convert_and_compare(input_data, model_spec, expected_results, atol, rtol, backend, converter_input_type, compute_unit, minimum_deployment_target) and returns: model_spec,  mlmodel,  coreml_inputs,  coreml_preds, model_spec,  mlmodel,  coreml_inputs,  None