code: def synchronize():
      """
      Helper function to synchronize (barrier) among all processes when
      using distributed training
      """
      if not dist.is_available():
          return
      if not dist.is_initialized():
          return
      world_size = dist.get_world_size()
      if world_size == 1:
          return
      if dist.get_backend() == dist.Backend.NCCL:
          # This argument is needed to avoid warnings.
          # It's valid only for NCCL backend.
          dist.barrier(device_ids=[torch.cuda.current_device()])
      else:
          dist.barrier()
  
file_path: detectron2/detectron2/utils/comm.py
imports: import functools
  import numpy
  import torch
  import torch.distributed
label_desc: Use image node_id 8 for calling a global function with example usage: synchronize() without return types