code: def check_output_types(self, func, ref_outputs, args, kwargs):
      graph = getattr(func, "last_graph", None)
      types = [o.type() for o in graph.outputs()]
      self.assertTrue(len(types) == 1)
      t = types[0]
      torch._C._jit_assert_is_instance(ref_outputs, t)
  
imports: import torch
  import torch.cuda
  import torch.jit
  import torch.jit._logging
  import torch.jit.frontend
  import torch.jit.quantized
  from torch.testing._internal.common_dtype import floating_and_complex_types_and
  from torch.testing._internal.common_utils import TestCase, freeze_rng_state, TemporaryFileName, enable_profiling_mode_for_profiling_tests, is_iterable_of_tensors
  from torch.testing._internal.common_utils import enable_profiling_mode
  from itertools import chain
  from typing import List, Union
  from torch._C import TensorType
  import io
label_desc: Use image node_id 1 for calling a global function with example usage: check_output_types(func, ref_outputs, args, kwargs) without return types