code: def test_repr(self, device, dtype, module_info, training):
      # Test module can be represented with repr and str without errors.
      module_cls = module_info.module_cls
      module_inputs = module_info.module_inputs_func(
          module_info,
          device=device,
          dtype=dtype,
          requires_grad=False,
          training=training,
      )
      for module_input in module_inputs:
          args, kwargs = (
              module_input.constructor_input.args,
              module_input.constructor_input.kwargs,
          )
          m = module_cls(*args, **kwargs)
          m.to(device).to(dtype)
          m.train(training)
  
          # Check that these methods do not raise errors
          m.__repr__()
          str(m)
  
imports: from itertools import chain, product
  from inspect import signature, isgenerator
  from copy import deepcopy
  import tempfile
  from operator import methodcaller
  import torch
  from torch._subclasses.meta_utils import assert_metadata_eq
  from torch.testing._internal.common_cuda import with_tf32_off
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyCPU, onlyCUDA, toleranceOverride, tol, skipMeta
  from torch.testing._internal.common_modules import module_db, modules, ModuleErrorEnum, TrainEvalMode
  from torch.testing._internal.common_utils import TestCase, run_tests, freeze_rng_state, mock_wrapper, get_tensors_from, gradcheck, gradgradcheck
  from unittest.mock import patch, call
label_desc: Use image node_id 5 for calling the TestModule obj's underlying member method code with example usage: obj.test_repr(device, dtype, module_info, training) without return types