code: def test_torch_result_type(
      dtypes_and_xs,
      on_device,
      fn_tree,
      frontend,
      test_flags,
      backend_fw,
  ):
      x1, x2, input_dtypes, num_pos_args = dtypes_and_xs
      if num_pos_args is not None:
          test_flags.num_positional_args = num_pos_args
      helpers.test_frontend_function(
          input_dtypes=input_dtypes,
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          tensor=x1,
          other=x2,
      )
  
imports: from hypothesis import strategies
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
label_desc: Use image node_id 3 for calling a global function with example usage: test_torch_result_type(dtypes_and_xs, on_device, fn_tree, frontend, test_flags, backend_fw) without return types