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", help="path to dataset")
      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(
          "--prof",
          default=-1,
          type=int,
          help="Only run 10 iterations for profiling.",
      )
      parser.add_argument("--deterministic", action="store_true")
  
      parser.add_argument(
          "--local_rank", default=os.getenv("LOCAL_RANK", 0), type=int
      )
      parser.add_argument(
          "--sync_bn",
          action="store_true",
          help="enabling apex sync BN.",
      )
  
      parser.add_argument("--opt-level", type=str)
      parser.add_argument(
          "--keep-batchnorm-fp32", type=str, default=None
      )
      parser.add_argument("--loss-scale", type=str, default=None)
      parser.add_argument("--channels-last", type=bool, default=False)
      args = parser.parse_args()
      return args
  
imports: import argparse
  import os
  import shutil
  import time
  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 torchvision.transforms
  import torchvision.datasets
  import torchvision.models
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: parse() and returns: args