code: def test_factory_kwargs(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,
      )
      for module_input in module_inputs:
          args, kwargs = (
              module_input.constructor_input.args,
              module_input.constructor_input.kwargs,
          )
  
          # Check if this module creates parameters or registers buffers.
          # The mock magic here passes through to the real Parameter / register_buffer
          # logic and is only used to check call inputs.
          module_creates_params_or_buffers = False
          parameter_new = mock_wrapper(torch.nn.Parameter.__new__)
          with patch.object(
              torch.nn.Parameter, "__new__", parameter_new
          ):
              register_buffer = mock_wrapper(
                  torch.nn.Module.register_buffer
              )
              with patch.object(
                  torch.nn.Module, "register_buffer", register_buffer
              ):
                  m = module_cls(*args, **kwargs)
                  m.train(training)
  
                  # Check if a parameter or buffer was created with a tensor not passed to the constructor.
                  constructor_tensors = get_tensors_from(args, kwargs)
                  for mock in [
                      parameter_new.mock,
                      register_buffer.mock,
                  ]:
                      for call_args, call_kwargs in mock.call_args_list:
                          call_tensors = get_tensors_from(
                              call_args, call_kwargs
                          )
                          if len(
                              call_tensors
                          ) > 0 and not constructor_tensors.intersection(
                              call_tensors
                          ):
                              module_creates_params_or_buffers = True
                              break
  
          if not module_creates_params_or_buffers:
              continue
  
          # Instantiate module with the factory kwargs.
          kwargs.update(
              {
                  "device": device,
                  "dtype": dtype,
              }
          )
  
          if issubclass(
              module_info.module_cls,
              torch.nn.modules.lazy.LazyModuleMixin,
          ):
              # Ensure device and dtype are passed to all UninitializedParameters and UninitializedBuffers.
              uninit_param_new = mock_wrapper(
                  torch.nn.UninitializedParameter.__new__
              )
              with patch.object(
                  torch.nn.UninitializedParameter,
                  "__new__",
                  uninit_param_new,
              ):
                  uninit_buffer_new = mock_wrapper(
                      torch.nn.UninitializedBuffer.__new__
                  )
                  with patch.object(
                      torch.nn.UninitializedBuffer,
                      "__new__",
                      uninit_buffer_new,
                  ):
                      m = module_cls(*args, **kwargs)
                      m.train(training)
                      uninit_param_new.mock.assert_has_calls(
                          [
                              call(device=device, dtype=dtype)
                              for _ in uninit_param_new.mock.mock_calls
                          ]
                      )
                      uninit_buffer_new.mock.assert_has_calls(
                          [
                              call(device=device, dtype=dtype)
                              for _ in uninit_buffer_new.mock.mock_calls
                          ]
                      )
          else:
              # Check device placement and dtype for created parameters and buffers.
              # Only verify floating point dtypes since that's what the kwarg applies to.
              m = module_cls(*args, **kwargs)
              m.train(training)
              self._assert_module_parameters_and_buffer_are(
                  m, device, dtype
              )
  
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 3 for calling the TestModule obj's underlying member method code with example usage: obj.test_factory_kwargs(device, dtype, module_info, training) without return types