code: def main_worker(gpu, ngpus_per_node, main, cfg):
      """The main_worker process function (on individual GPU)"""
      cudnn.benchmark = True
  
      cfg.DDP_CONFIG.GPU = gpu
      print("Use GPU: {}".format(gpu))
  
      if cfg.DDP_CONFIG.DISTRIBUTED:
          cfg.DDP_CONFIG.GPU_WORLD_RANK = (
              cfg.DDP_CONFIG.WORLD_RANK * ngpus_per_node + gpu
          )
          dist.init_process_group(
              backend=cfg.DDP_CONFIG.DIST_BACKEND,
              init_method=cfg.DDP_CONFIG.DIST_URL,
              world_size=cfg.DDP_CONFIG.GPU_WORLD_SIZE,
              rank=cfg.DDP_CONFIG.GPU_WORLD_RANK,
          )
      main(cfg)
  
imports: import torch
  import torch.distributed
  import torch.backends.cudnn
  import torch.multiprocessing
label_desc: Use image node_id 3 for calling a global function with example usage: main_worker(gpu, ngpus_per_node, main, cfg) without return types