code: def parse():
      model_names = sorted(
          name
          for name in models.__dict__
          if name.islower()
          and not name.startswith("__")
          and callable(models.__dict__[name])
      )
  
      parser = argparse.ArgumentParser(
          description="PyTorch ImageNet Training"
      )
      parser.add_argument(
          "data",
          metavar="DIR",
          nargs="*",
          help="path(s) to dataset (if one path is provided, it is assumed\n"
          + 'to have subdirectories named "train" and "val"; alternatively,\n'
          + "train and val paths can be specified directly by providing both paths as arguments)",
      )
      parser.add_argument(
          "--arch",
          "-a",
          metavar="ARCH",
          default="resnet18",
          choices=model_names,
          help="model architecture: "
          + " | ".join(model_names)
          + " (default: resnet18)",
      )
      parser.add_argument(
          "-j",
          "--workers",
          default=4,
          type=int,
          metavar="N",
          help="number of data loading workers (default: 4)",
      )
      parser.add_argument(
          "--epochs",
          default=90,
          type=int,
          metavar="N",
          help="number of total epochs to run",
      )
      parser.add_argument(
          "--start-epoch",
          default=0,
          type=int,
          metavar="N",
          help="manual epoch number (useful on restarts)",
      )
      parser.add_argument(
          "-b",
          "--batch-size",
          default=256,
          type=int,
          metavar="N",
          help="mini-batch size per process (default: 256)",
      )
      parser.add_argument(
          "--lr",
          "--learning-rate",
          default=0.1,
          type=float,
          metavar="LR",
          help="Initial learning rate.  Will be scaled by <global batch size>/256: args.lr = args.lr*float(args.batch_size*args.world_size)/256.  A warmup schedule will also be applied over the first 5 epochs.",
      )
      parser.add_argument(
          "--momentum",
          default=0.9,
          type=float,
          metavar="M",
          help="momentum",
      )
      parser.add_argument(
          "--weight-decay",
          "--wd",
          default=1e-4,
          type=float,
          metavar="W",
          help="weight decay (default: 1e-4)",
      )
      parser.add_argument(
          "--print-freq",
          "-p",
          default=10,
          type=int,
          metavar="N",
          help="print frequency (default: 10)",
      )
      parser.add_argument(
          "--resume",
          default="",
          type=str,
          metavar="PATH",
          help="path to latest checkpoint (default: none)",
      )
      parser.add_argument(
          "-e",
          "--evaluate",
          dest="evaluate",
          action="store_true",
          help="evaluate model on validation set",
      )
      parser.add_argument(
          "--pretrained",
          dest="pretrained",
          action="store_true",
          help="use pre-trained model",
      )
  
      parser.add_argument(
          "--dali_cpu",
          action="store_true",
          help="Runs CPU based version of DALI pipeline.",
      )
      parser.add_argument(
          "--disable_dali",
          default=False,
          action="store_true",
          help="Disable DALI data loader and use native PyTorch one instead.",
      )
      parser.add_argument(
          "--prof",
          default=-1,
          type=int,
          help="Only run 10 iterations for profiling.",
      )
      parser.add_argument("--deterministic", action="store_true")
  
      parser.add_argument(
          "--fp16-mode",
          default=False,
          action="store_true",
          help="Enable half precision mode.",
      )
      parser.add_argument("--loss-scale", type=float, default=1)
      parser.add_argument("--channels-last", type=bool, default=False)
      parser.add_argument(
          "-t",
          "--test",
          action="store_true",
          help="Launch test mode with preset arguments",
      )
      args = parser.parse_args()
      return args
  
imports: import argparse
  import os
  import shutil
  import time
  import math
  import torch
  import torch.nn
  import torch.nn.parallel
  import torch.backends.cudnn
  import torch.distributed
  import torch.optim
  import torch.utils.data
  import torch.utils.data.distributed
  import numpy
  from torch.nn.parallel import DistributedDataParallel
  import torchvision.transforms
  import torchvision.datasets
  import torchvision.models
label_desc: Use image node_id 2 for calling a global function with example usage: parse() and returns: args