def METHOD_NAME(self):
    """Tests that customized state dict methods are in effect"""
    class CustomStateDictModule(torch.nn.Module):
        def __init__(self):
            super().__init__()
            self.conv = torch.nn.Conv2d(6, 16, 5)
            self.fc = torch.nn.Linear(16 * 5 * 5, 120)
            self.customized_save_state_dict_called: bool = False
            self.customized_load_state_dict_called: bool = False
        def forward(self, x):
            x = self.conv(x)
            x = self.fc(x)
            return x
        @torch.jit.export
        def _save_to_state_dict(self, destination: Dict[str, torch.Tensor],
                                prefix: str, keep_vars: bool):
            self.customized_save_state_dict_called = True
            return {"dummy": torch.ones(1)}
        @torch.jit.export
        def _load_from_state_dict(self,
                                  state_dict: Dict[str, torch.Tensor],
                                  prefix: str, local_metadata: Any,
                                  strict: bool, missing_keys: List[str],
                                  unexpected_keys: List[str],
                                  error_msgs: List[str]):
            self.customized_load_state_dict_called = True
            return
    m1 = torch.jit.script(CustomStateDictModule())
    self.assertFalse(m1.customized_save_state_dict_called)
    state_dict = m1.state_dict()
    self.assertTrue(m1.customized_save_state_dict_called)
    m2 = torch.jit.script(CustomStateDictModule())
    self.assertFalse(m2.customized_load_state_dict_called)
    m2.load_state_dict(state_dict)
    self.assertTrue(m2.customized_load_state_dict_called)