code: def test_module(self):
      class M(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.linear1 = torch.nn.Linear(3, 3)
              self.relu = torch.nn.ReLU()
              self.linear2 = torch.nn.Linear(3, 5)
  
          def forward(self, x):
              x = self.linear1(x)
              x = self.linear1(x)
              x = torch.nn.functional.relu(x)
              x = self.linear2(x)
              return x
  
      inputs = (torch.randn(3, 3),)
      self.check_graph(M(), inputs)
  
imports: import io
  import pathlib
  import tempfile
  import unittest
  import zipfile
  import torch
  import torch._dynamo
  from torch.export import export, save, load, Dim
  from torch._export.db.case import ExportCase, normalize_inputs, SupportLevel
  from torch._export.db.examples import all_examples
  from torch._export.serde.serialize import ExportedProgramDeserializer, ExportedProgramSerializer, canonicalize, deserialize, serialize, SerializeError
  from torch._subclasses.fake_tensor import FakeTensor
  from torch.fx.experimental.symbolic_shapes import is_concrete_int
  import torch.utils._pytree
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests, TestCase, TemporaryFileName, IS_FBCODE, IS_MACOS, IS_SANDCASTLE, IS_WINDOWS, find_library_location
label_desc: Use image node_id 7 for calling the TestDeserialize obj's underlying member method code with example usage: obj.test_module() and returns: x