code: def parse_args():
      """Parse the user arguments."""
      parser = argparse.ArgumentParser(
          description="Finetuning for PFLD"
      )
      parser.add_argument(
          "--dev_id", dest="dev_id", default="0", type=str
      )
      parser.add_argument("--base_lr", default=0.0001, type=int)
      parser.add_argument(
          "--weight-decay", "--wd", default=1e-6, type=float
      )
      parser.add_argument("--img_size", default=112, type=int)
      parser.add_argument(
          "--supernet", default="", type=str, metavar="PATH"
      )
      parser.add_argument("--end_epoch", default=300, type=int)
      parser.add_argument(
          "--snapshot", default="models", type=str, metavar="PATH"
      )
      parser.add_argument("--log_file", default="train.log", type=str)
      parser.add_argument(
          "--data_root", default="/dataset", type=str, metavar="PATH"
      )
      parser.add_argument("--train_batchsize", default=256, type=int)
      parser.add_argument("--val_batchsize", default=128, type=int)
      args = parser.parse_args()
      args.snapshot = os.path.join(args.snapshot, "subnet")
      args.log_file = os.path.join(
          args.snapshot, "{}.log".format("subnet")
      )
      os.makedirs(args.snapshot, exist_ok=True)
      return args
  
file_path: nni/examples/nas/legacy/oneshot/pfld/retrain.py
imports: from __future__ import absolute_import, division, print_function
  import argparse
  import logging
  import os
  import time
  import torch
  import torchvision
  import numpy
  from datasets import PFLDDatasets
  from lib.builder import search_space
  from lib.ops import PRIMITIVES
  from lib.utils import PFLDLoss, accuracy
  from nni.algorithms.nas.pytorch.fbnet import LookUpTable, NASConfig, supernet_sample
  from nni.nas.pytorch.utils import AverageMeter
  from torch.utils.data import DataLoader
label_desc: Use image node_id 5 for calling a global function with example usage: parse_args() and returns: args