code: def _parse_and_validate_remote_device(pg, remote_device):
      if remote_device is None:
          raise ValueError("remote device is None")
  
      worker_name = remote_device.worker_name()
      rank = remote_device.rank()
      device = remote_device.device()
  
      # Validate rank, skip validation if rank is not part of process group.
      if not distributed_c10d._rank_not_in_group(pg):
          if rank is not None and (
              rank < 0 or rank >= distributed_c10d.get_world_size(pg)
          ):
              raise ValueError(f"Invalid rank: {rank}")
  
      if worker_name is not None:
          if not rpc._is_current_rpc_agent_set():
              raise RuntimeError(
                  f"RPC framework needs to be initialized for using worker names: {worker_name}"
              )
  
          workers = rpc._get_current_rpc_agent().get_worker_infos()
          for worker in workers:
              if worker.name == worker_name:
                  return worker.id, device
  
          raise ValueError(f"Invalid worker name: {worker_name}")
  
      return rank, device
  
file_path: pytorch/torch/distributed/_shard/sharded_tensor/utils.py
imports: import collections.abc
  import copy
  from typing import Optional, List, Sequence
  import torch
  from torch.distributed import distributed_c10d
  from torch.distributed import rpc
  from torch.distributed._shard.sharding_spec._internals import check_tensor, validate_non_overlapping_shards_metadata
  from torch.distributed._shard.metadata import ShardMetadata
  from .metadata import TensorProperties, ShardedTensorMetadata
  from .shard import Shard
label_desc: Use image node_id 1 for calling a global function with example usage: _parse_and_validate_remote_device(pg, remote_device) and returns: rank,  device, worker,  device