code: def test_handle_return_return_inputs_as_outputs():
      @mb.program(
          input_specs=[
              mb.TensorSpec(shape=(1, 2)),
              mb.TensorSpec(shape=(1, 2)),
          ]
      )
      def prog(a, b):
          return mb.mul(x=a, y=2), b
  
      prev_main_output_names = [o.name for o in prog["main"].outputs]
      assert (
          prog["main"].outputs[1].op is None
      )  # output comes from input
  
      prev_prog = copy.deepcopy(prog)
      PASS_REGISTRY["nn_backend::handle_return_inputs_as_outputs"](prog)
      assert_same_output_names(prev_prog, prog)
  
      assert (
          prog["main"].outputs[1].op is not None
      )  # output comes from an op
      assert prog["main"].outputs[1].op.op_type == "identity"
  
      assert_model_is_valid(prog, {"a": (1, 2), "b": (1, 2)})
  
imports: import copy
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil.passes.pass_registry import PASS_REGISTRY
  from coremltools.converters.mil.testing_utils import assert_model_is_valid
  from coremltools.converters.mil.testing_utils import assert_same_output_names
label_desc: Use image node_id 2 for calling a global function with example usage: test_handle_return_return_inputs_as_outputs() and returns: mb,  b