code: def main():
      args = parser.parse_args()
      bps.init()
  
      if args.seed is not None:
          random.seed(args.seed)
          torch.manual_seed(args.seed)
          cudnn.deterministic = True
          warnings.warn(
              "You have chosen to seed training. "
              "This will turn on the CUDNN deterministic setting, "
              "which can slow down your training considerably! "
              "You may see unexpected behavior when restarting "
              "from checkpoints."
          )
  
      if args.gpu is not None:
          warnings.warn(
              "You have chosen a specific GPU. This will completely "
              "disable data parallelism."
          )
  
      if args.dist_url == "env://" and args.world_size == -1:
          args.world_size = int(os.environ["WORLD_SIZE"])
  
      args.distributed = (
          args.world_size > 1 or args.multiprocessing_distributed
      )
  
      ngpus_per_node = torch.cuda.device_count()
      if args.multiprocessing_distributed:
          # Since we have ngpus_per_node processes per node, the total world_size
          # needs to be adjusted accordingly
          args.world_size = ngpus_per_node * args.world_size
          # Use torch.multiprocessing.spawn to launch distributed processes: the
          # main_worker process function
          args.gpu = bps.local_rank()
          main_worker(args.gpu, ngpus_per_node, args)
      else:
          # Simply call main_worker function
          main_worker(args.gpu, ngpus_per_node, args)
  
imports: import argparse
  import os
  import random
  import shutil
  import time
  import warnings
  import torch
  import torch.nn
  import torch.nn.parallel
  import torch.backends.cudnn
  import torch.distributed
  import torch.optim
  import torch.multiprocessing
  import torch.utils.data
  import torch.utils.data.distributed
  import torchvision.transforms
  import torchvision.datasets
  import torchvision.models
  from byteps.torch.parallel import DistributedDataParallel
  import byteps.torch
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types