code: def test_predict_api(self):
      model = MLModel(self.spec)
  
      package = tempfile.TemporaryDirectory(suffix=".mlpackage")
      package.cleanup()
  
      model.save(package.name)
  
      if utils._macos_version() >= (12, 0):
          for compute_units in coremltools.ComputeUnit:
              if (
                  compute_units == coremltools.ComputeUnit.CPU_AND_NE
                  and utils._macos_version() < (13, 0)
              ):
                  continue
  
              loaded_model = MLModel(
                  package.name, compute_units=compute_units
              )
  
              preds = loaded_model.predict(
                  {"feature_1": 1.0, "feature_2": 1.0}
              )
              assert preds is not None
              assert len(preds.keys()) == 1
              assert preds["output"] == 3.1
              assert loaded_model.compute_unit == compute_units
      else:
          # just check if we can load it
          loaded_model = MLModel(package.name)
  
      # cleanup
      _remove_path(package.name)
  
imports: import os
  import shutil
  import tempfile
  import numpy
  import pytest
  import coremltools
  from coremltools import ComputeUnit, utils
  from coremltools._deps import _HAS_TORCH
  from coremltools.converters.mil import Builder
  from coremltools.libmodelpackage import ModelPackage
  from coremltools.models import MLModel
  from coremltools.models.utils import _MLPACKAGE_AUTHOR_NAME, _WEIGHTS_DIR_NAME
  from coremltools.proto import Model_pb2
label_desc: Use image node_id 5 for calling the TestMLModel obj's underlying member method code with example usage: obj.test_predict_api() without return types