code: def enable(
      *,
      set_cuda_current_device: bool = True,
      overwrite: bool = False,
      allow_nccl_timeout: bool = False,
  ):
      """Enable distributed mode
  
      Args:
          set_cuda_current_device: If True, call torch.cuda.set_device() to set the
              current PyTorch CUDA device to the one matching the local rank.
          overwrite: If True, overwrites already set variables. Else fails.
      """
  
      global _LOCAL_RANK, _LOCAL_WORLD_SIZE
      if _LOCAL_RANK >= 0 or _LOCAL_WORLD_SIZE >= 0:
          raise RuntimeError(
              "Distributed mode has already been enabled"
          )
      torch_env = _TorchDistributedEnvironment()
      torch_env.export(overwrite=overwrite)
  
      if set_cuda_current_device:
          torch.cuda.set_device(torch_env.local_rank)
  
      if allow_nccl_timeout:
          # This allows to use torch distributed timeout in a NCCL backend
          key, value = "NCCL_ASYNC_ERROR_HANDLING", "1"
          if not overwrite:
              _check_env_variable(key, value)
          os.environ[key] = value
  
      dist.init_process_group(backend="nccl")
      dist.barrier()
  
      # Finalize setup
      _LOCAL_RANK = torch_env.local_rank
      _LOCAL_WORLD_SIZE = torch_env.local_world_size
      _restrict_print_to_main_process()
  
imports: import os
  import random
  import re
  import socket
  from typing import Dict, List
  import torch
  import torch.distributed
label_desc: Use image node_id 14 for calling a global function with example usage: enable() without return types