code: def init_rpc(
      current_ctx: DistContext,
      master_addr: str,
      master_port: int,
      num_rpc_threads: int = 16,
      rpc_timeout: float = 240.0,
      rpc_worker_names: Optional[Dict[DistRole, List[str]]] = None,
  ):
      with _rpc_init_lock:
          if rpc_is_initialized():
              return
  
          if current_ctx is None:
              raise RuntimeError(
                  "'dist_context' has not been set in 'init_rpc'"
              )
  
          options = rpc.TensorPipeRpcBackendOptions(
              _transports=["ibv", "uv"],
              _channels=["mpt_uv", "basic"],
              num_worker_threads=num_rpc_threads,
              rpc_timeout=rpc_timeout,
              init_method=f"tcp://{master_addr}:{master_port}",
          )
  
          rpc.init_rpc(
              name=current_ctx.worker_name,
              rank=current_ctx.global_rank,
              world_size=current_ctx.global_world_size,
              rpc_backend_options=options,
          )
  
          global_barrier(timeout=rpc_timeout)
  
imports: import logging
  import threading
  from abc import ABC, abstractmethod
  from typing import Any, Callable, Dict, List, Optional
  from torch.distributed import rpc
  from torch_geometric.distributed.dist_context import DistContext, DistRole
label_desc: Use image node_id 5 for calling a global function with example usage: init_rpc(current_ctx, master_addr, master_port, num_rpc_threads, rpc_timeout, rpc_worker_names) without return types