code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "-n",
          "--nodes",
          default=4,
          type=int,
          metavar="N",
          help="number of data loading workers (default: 4)",
      )
      parser.add_argument(
          "-g",
          "--gpus",
          default=1,
          type=int,
          help="number of gpus per node",
      )
      parser.add_argument(
          "-nr",
          "--nr",
          default=0,
          type=int,
          help="ranking within the nodes",
      )
      parser.add_argument(
          "--epochs",
          default=2,
          type=int,
          metavar="N",
          help="number of total epochs to run",
      )
      args = parser.parse_args()
      args.world_size = args.gpus * args.nodes
      os.environ["MASTER_ADDR"] = "10.57.23.164"
      os.environ["MASTER_PORT"] = "8888"
      mp.spawn(train, nprocs=args.gpus, args=(args,))
  
file_path: byteps/example/pytorch/mnist-distributed.py
imports: import os
  from datetime import datetime
  import argparse
  import torch.multiprocessing
  import torchvision
  import torchvision.transforms
  import torch
  import torch.nn
  import torch.distributed
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types