code: def _assert_module_parameters_and_buffer_are(
      self, module, device, dtype
  ):
      # Check device placement and dtype for created parameters and buffers.
      # Only verify floating point dtypes since that's what the kwarg or methods
      # such as `float()` applies to.
      if not isinstance(device, torch.device):
          device = torch.device(device)
  
      def _check_module(items, name, device=device, dtype=dtype):
          for item_name, item in items:
              self.assertEqual(
                  item.device,
                  device,
                  f"{name} {item_name} is on device {item.device} instead of the expected device {device}",
              )
              if item.dtype.is_floating_point:
                  self.assertEqual(
                      item.dtype,
                      dtype,
                      f"{name} {item_name} is of dtype {item.dtype} instead of the expected dtype {dtype}",
                  )
  
      _check_module(module.named_parameters(), "Parameter")
      _check_module(module.named_buffers(), "Buffer")
  
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 1 for calling the TestModule obj's underlying member method code with example usage: obj._assert_module_parameters_and_buffer_are(module, device, dtype) without return types