code: def parse_args():
      import argparse
  
      parser = argparse.ArgumentParser(
          description="pytorch u2net training"
      )
  
      parser.add_argument("--data-path", default="./", help="DUTS root")
      parser.add_argument(
          "--device", default="cuda", help="training device"
      )
      parser.add_argument("-b", "--batch-size", default=16, type=int)
      parser.add_argument(
          "--wd",
          "--weight-decay",
          default=1e-4,
          type=float,
          metavar="W",
          help="weight decay (default: 1e-4)",
          dest="weight_decay",
      )
      parser.add_argument(
          "--epochs",
          default=360,
          type=int,
          metavar="N",
          help="number of total epochs to train",
      )
      parser.add_argument(
          "--eval-interval",
          default=10,
          type=int,
          help="validation interval default 10 Epochs",
      )
  
      parser.add_argument(
          "--lr",
          default=0.001,
          type=float,
          help="initial learning rate",
      )
      parser.add_argument(
          "--print-freq", default=50, type=int, help="print frequency"
      )
      parser.add_argument(
          "--resume", default="", help="resume from checkpoint"
      )
      parser.add_argument(
          "--start-epoch",
          default=0,
          type=int,
          metavar="N",
          help="start epoch",
      )
      # Mixed precision training parameters
      parser.add_argument(
          "--amp",
          action="store_true",
          help="Use torch.cuda.amp for mixed precision training",
      )
  
      args = parser.parse_args()
  
      return args
  
file_path: deep-learning-for-image-processing/pytorch_segmentation/u2net/train.py
imports: import os
  import time
  import datetime
  from typing import Union, List
  import torch
  from torch.utils import data
  from src import u2net_full
  from train_utils import train_one_epoch, evaluate, get_params_groups, create_lr_scheduler
  from my_dataset import DUTSDataset
  import transforms
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args() and returns: args