code: def test_module_export_operator_list(self):
      class Foo(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.weight = torch.ones((20, 1, 5, 5))
              self.bias = torch.ones(20)
  
          def forward(self, input):
              x1 = torch.zeros(2, 2)
              x2 = torch.empty_like(torch.empty(2, 2))
              x3 = torch._convolution(
                  input,
                  self.weight,
                  self.bias,
                  [1, 1],
                  [0, 0],
                  [1, 1],
                  False,
                  [0, 0],
                  1,
                  False,
                  False,
                  True,
                  True,
              )
              return (x1, x2, x3)
  
      m = torch.jit.script(Foo())
  
      buffer = io.BytesIO(m._save_to_buffer_for_lite_interpreter())
      buffer.seek(0)
      mobile_module = _load_for_lite_interpreter(buffer)
  
      expected_ops = {
          "aten::_convolution",
          "aten::empty.memory_format",
          "aten::empty_like",
          "aten::zeros",
      }
      actual_ops = _export_operator_list(mobile_module)
      self.assertEqual(actual_ops, expected_ops)
  
imports: import torch
  import torch.utils.bundled_inputs
  import io
  from tempfile import TemporaryFileName
  from typing import Dict, List
  import inspect
  from torch.testing import FileCheck
  from torch.jit.mobile import _load_for_lite_interpreter, _export_operator_list
  from torch.testing._internal.common_utils import TestCase, run_tests
  from torch.testing._internal.common_quantization import AnnotatedSingleLayerLinearModel, TwoLayerLinearModel, AnnotatedNestedModel
  from torch.testing._internal.common_quantization import QuantizationLiteTestCase
label_desc: Use image node_id 10 for calling the TestLiteScriptModule obj's underlying member method code with example usage: obj.test_module_export_operator_list() and returns: x1,  x2,  x3