code: def _test_state_dict_device(
      self, config, rank, group, pure_fp16=False, **model_kwargs
  ):
      model = TransformerWithSharedParams(group, **model_kwargs)
      if pure_fp16:
          assert not config["mixed_precision"]
          model = model.half()
      fsdp_model = FSDP(model, group, **config)
      if not config["cpu_offload"]:
          fsdp_model = fsdp_model.cuda()
      autocast = fsdp_model.mixed_precision or pure_fp16
      self._train_for_several_steps(fsdp_model, 1, autocast)
  
      state_dict = fsdp_model.state_dict()
  
      state_dict_device = config.get("state_dict_device")
      for k, v in state_dict.items():
          if config["cpu_offload"] or (
              state_dict_device is not None
              and state_dict_device.type == "cpu"
          ):
              assert v.device.type == "cpu", v.device.type
          else:
              assert v.device.type == "cuda", v.device.type
  
      expected_dtype = torch.float16 if pure_fp16 else torch.float32
      for k, v in state_dict.items():
          if not torch.is_floating_point(v):
              continue
          assert (
              v.dtype == expected_dtype
          ), f"{v.dtype} != {expected_dtype}"
  
imports: import functools
  import unittest
  from parameterized import parameterized
  import pytest
  import torch
  from torch import nn
  from fairscale.fair_dev.testing.testing import dist_init, objects_are_equal, skip_if_cuda, teardown, temp_files_ctx
  from fairscale.internal import torch_version
  from fairscale.nn.data_parallel import FullyShardedDataParallel
  from .test_fsdp import CONFIG_OPTIONS, DistributedTest, NestedWrappedModule, TransformerWithSharedParams, rename_test, spawn_and_init
label_desc: Use image node_id 5 for calling the TestStateDictDeviceDtype obj's underlying member method code with example usage: obj._test_state_dict_device(config, rank, group, pure_fp16) without return types