code: def get_args_parser(add_help=True):
      import argparse
  
      parser = argparse.ArgumentParser(
          description="PyTorch Segmentation Training", add_help=add_help
      )
  
      parser.add_argument(
          "--data-path",
          default="/datasets01/COCO/022719/",
          type=str,
          help="dataset path",
      )
      parser.add_argument(
          "--dataset", default="coco", type=str, help="dataset name"
      )
      parser.add_argument(
          "--model",
          default="fcn_resnet101",
          type=str,
          help="model name",
      )
      parser.add_argument(
          "--aux-loss", action="store_true", help="auxiliary loss"
      )
      parser.add_argument(
          "--device",
          default="cuda",
          type=str,
          help="device (Use cuda or cpu Default: cuda)",
      )
      parser.add_argument(
          "-b",
          "--batch-size",
          default=8,
          type=int,
          help="images per gpu, the total batch size is $NGPU x batch_size",
      )
      parser.add_argument(
          "--epochs",
          default=30,
          type=int,
          metavar="N",
          help="number of total epochs to run",
      )
  
      parser.add_argument(
          "-j",
          "--workers",
          default=16,
          type=int,
          metavar="N",
          help="number of data loading workers (default: 16)",
      )
      parser.add_argument(
          "--lr", default=0.01, type=float, help="initial learning rate"
      )
      parser.add_argument(
          "--momentum",
          default=0.9,
          type=float,
          metavar="M",
          help="momentum",
      )
      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(
          "--lr-warmup-epochs",
          default=0,
          type=int,
          help="the number of epochs to warmup (default: 0)",
      )
      parser.add_argument(
          "--lr-warmup-method",
          default="linear",
          type=str,
          help="the warmup method (default: linear)",
      )
      parser.add_argument(
          "--lr-warmup-decay",
          default=0.01,
          type=float,
          help="the decay for lr",
      )
      parser.add_argument(
          "--print-freq", default=10, type=int, help="print frequency"
      )
      parser.add_argument(
          "--output-dir",
          default=".",
          type=str,
          help="path to save outputs",
      )
      parser.add_argument(
          "--resume", default="", type=str, help="path of checkpoint"
      )
      parser.add_argument(
          "--start-epoch",
          default=0,
          type=int,
          metavar="N",
          help="start epoch",
      )
      parser.add_argument(
          "--test-only",
          dest="test_only",
          help="Only test the model",
          action="store_true",
      )
      parser.add_argument(
          "--use-deterministic-algorithms",
          action="store_true",
          help="Forces the use of deterministic algorithms only.",
      )
      # distributed training parameters
      parser.add_argument(
          "--world-size",
          default=1,
          type=int,
          help="number of distributed processes",
      )
      parser.add_argument(
          "--dist-url",
          default="env://",
          type=str,
          help="url used to set up distributed training",
      )
  
      parser.add_argument(
          "--weights",
          default=None,
          type=str,
          help="the weights enum name to load",
      )
      parser.add_argument(
          "--weights-backbone",
          default=None,
          type=str,
          help="the backbone weights enum name to load",
      )
  
      # Mixed precision training parameters
      parser.add_argument(
          "--amp",
          action="store_true",
          help="Use torch.cuda.amp for mixed precision training",
      )
  
      parser.add_argument(
          "--backend",
          default="PIL",
          type=str.lower,
          help="PIL or tensor - case insensitive",
      )
      parser.add_argument(
          "--use-v2", action="store_true", help="Use V2 transforms"
      )
      return parser
  
file_path: vision/references/segmentation/train.py
imports: import datetime
  import os
  import time
  import warnings
  import presets
  import torch
  import torch.utils.data
  import torchvision
  import utils
  from coco_utils import get_coco
  from torch import nn
  from torch.optim.lr_scheduler import PolynomialLR
  from torchvision.transforms import functional, InterpolationMode
label_desc: Use image node_id 7 for calling a global function with example usage: get_args_parser(add_help) and returns: parser