code: def test_if_train_and_eval_modes_differ(
      self, device, dtype, module_info, training
  ):
      module_cls = module_info.module_cls
      module_inputs = module_info.module_inputs_func(
          module_info,
          device=device,
          dtype=dtype,
          requires_grad=False,
          training=training,
      )
  
      # Run forward inputs through to see if the training flag is accessed during forward.
      for module_input in module_inputs:
          if module_input.forward_input is None:
              continue
  
          # === Instantiate the module. ===
          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)
  
          # Remove training attribute and see if forward still works.
          delattr(m, "training")
  
          # === Do forward pass. ===
          try:
              args, kwargs = (
                  module_input.forward_input.args,
                  module_input.forward_input.kwargs,
              )
              m(*args, **kwargs)
          except AttributeError as e:
              if "'training'" in str(e):
                  self.assertTrue(
                      module_info.train_and_eval_differ,
                      f"The ModuleInfo entry for {module_info.name} has "
                      "train_and_eval_differ=False, but the training mode was found to "
                      "affect the forward pass. Consider setting train_and_eval_differ=True "
                      "for this ModuleInfo entry.",
                  )
              else:
                  raise e
  
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 18 for calling the TestModule obj's underlying member method code with example usage: obj.test_if_train_and_eval_modes_differ(device, dtype, module_info, training) without return types