code: def test_handle_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=2.0), 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"
  
      with pytest.raises(
          ValueError, match="used both as function's input and output"
      ):
          # prog has input and output names 'b' that refer to different vars
          # This program can pass if we disable 'dedup_op_and_var_names' pass
          assert_model_is_valid(prog, {"a": (1, 2), "b": (1, 2)})
  
file_path: coremltools/coremltools/converters/mil/backend/nn/passes/test_passes.py
imports: import copy
  import itertools
  import numpy
  import pytest
  from coremltools.converters.mil import testing_reqs
  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 apply_pass_and_basic_check, assert_model_is_valid, assert_same_output_names, get_op_types_in_program
label_desc: Use image node_id 2 for calling a global function with example usage: test_handle_return_inputs_as_outputs() and returns: mb,  b