code: def test_valid_device(self):
      if self.rank != 0:
          return
      dst_rank = (self.rank + 1) % self.world_size
      dst_worker_name = dist_utils.worker_name(dst_rank)
  
      for remote_module in self._create_remote_module_iter(
          f"{dst_worker_name}/cuda:0",
          modes=[ModuleCreationMode.MODULE_CTOR],
      ):
          device = rpc.rpc_sync(
              dst_worker_name,
              remote_device,
              (remote_module.module_rref,),
          )
          self.assertEqual(device.type, "cuda")
          self.assertEqual(device.index, 0)
  
      # Test rank works as well.
      for remote_module in self._create_remote_module_iter(
          f"rank:{dst_rank}/cuda:0",
          modes=[ModuleCreationMode.MODULE_CTOR],
      ):
          device = rpc.rpc_sync(
              dst_worker_name,
              remote_device,
              (remote_module.module_rref,),
          )
          self.assertEqual(device.type, "cuda")
          self.assertEqual(device.index, 0)
  
imports: import enum
  from typing import Tuple
  import torch
  import torch.distributed.rpc
  import torch.testing._internal.dist_utils
  from torch import Tensor, nn
  from torch._jit_internal import Future
  from torch.distributed.nn import RemoteModule
  from torch.distributed.nn.api.remote_module import _REMOTE_MODULE_PICKLED_ATTRIBUTES
  from torch.distributed.nn.api.remote_module import _RemoteModule
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  from torch.testing._internal.common_utils import TemporaryFileName
  from torch.testing._internal.distributed.rpc.rpc_agent_test_fixture import RpcAgentTestFixture
label_desc: Use image node_id 1 for calling the CudaRemoteModuleTest obj's underlying member method code with example usage: obj.test_valid_device() without return types