code: def test_device_ctx_init(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,
      )
      with torch.device("meta"):
          module_inputs_meta = module_info.module_inputs_func(
              module_info,
              device=None,
              dtype=dtype,
              requires_grad=False,
              training=training,
          )
  
      for module_input, module_input_meta in zip(
          module_inputs, module_inputs_meta
      ):
          c_args, c_kwargs = (
              module_input.constructor_input.args,
              module_input.constructor_input.kwargs,
          )
          fw_args, fw_kwargs = (
              module_input.forward_input.args,
              module_input.forward_input.kwargs,
          )
  
          c_args_meta, c_kwargs_meta = (
              module_input_meta.constructor_input.args,
              module_input_meta.constructor_input.kwargs,
          )
          fw_args_meta, fw_kwargs_meta = (
              module_input_meta.forward_input.args,
              module_input_meta.forward_input.kwargs,
          )
  
          m_cpu = module_cls(*c_args, **c_kwargs)
  
          with torch.device("meta"):
              m = module_cls(*c_args_meta, **c_kwargs_meta)
  
          for p_meta, p_cpu in chain(
              zip(m.parameters(), m_cpu.parameters()),
              zip(m.buffers(), m_cpu.buffers()),
          ):
              if torch.nn.parameter.is_lazy(p_meta):
                  continue
              self.assertTrue(p_meta.is_meta)
              assert_metadata_eq(self.assertEqual, p_meta, p_cpu)
  
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 19 for calling the TestModule obj's underlying member method code with example usage: obj.test_device_ctx_init(device, dtype, module_info, training) without return types