code: def main():
      args = parser.parse_args()
  
      if args.seed is not None:
          random.seed(args.seed)
          torch.manual_seed(args.seed)
          # torch.cuda.manual_seed_all(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
          or args.deepspeed
      )
  
      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
          mp.spawn(
              main_worker,
              nprocs=ngpus_per_node,
              args=(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
  from enum import Enum
  import deepspeed
  from deepspeed.runtime.data_pipeline.data_routing.helper import convert_to_random_ltd, save_without_random_ltd
  import torch
  import torch.nn
  import torch.nn.parallel
  import torch.backends.cudnn
  import torch.distributed
  import torch.optim
  from torch.optim.lr_scheduler import StepLR
  import torch.multiprocessing
  import torch.utils.data
  import torch.utils.data.distributed
  import torchvision.transforms
  import torchvision.datasets
  import torchvision.models
  from torch.utils.data import Subset
  import models
  from models.vit import Block
  import math
  from timm.data.transforms_factory import transforms_imagenet_eval, transforms_imagenet_train
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types