code: def parse_args():
      """Training Options for Semantic Segmentation Experiments"""
      parser = argparse.ArgumentParser(
          description="MXNet Gluon Semantic Segmentation"
      )
      # model and dataset
      parser.add_argument(
          "--model",
          type=str,
          default="fcn",
          help="model name (default: fcn)",
      )
      parser.add_argument(
          "--model-zoo",
          type=str,
          default=None,
          help="evaluating on model zoo model",
      )
      parser.add_argument(
          "--pretrained",
          action="store_true",
          help="whether to use pretrained params",
      )
      parser.add_argument(
          "--backbone",
          type=str,
          default="resnet50",
          help="backbone name (default: resnet50)",
      )
      parser.add_argument(
          "--dataset",
          type=str,
          default="pascal",
          help="dataset name (default: pascal)",
      )
      parser.add_argument(
          "--workers",
          type=int,
          default=16,
          metavar="N",
          help="dataloader threads",
      )
      parser.add_argument(
          "--base-size", type=int, default=520, help="base image size"
      )
      parser.add_argument(
          "--crop-size", type=int, default=480, help="crop image size"
      )
      parser.add_argument(
          "--train-split",
          type=str,
          default="train",
          help="dataset train split (default: train)",
      )
      # training hyper params
      parser.add_argument(
          "--aux",
          action="store_true",
          default=False,
          help="Auxiliary loss",
      )
      parser.add_argument(
          "--aux-weight",
          type=float,
          default=0.5,
          help="auxiliary loss weight",
      )
      parser.add_argument(
          "--epochs",
          type=int,
          default=50,
          metavar="N",
          help="number of epochs to train (default: 50)",
      )
      parser.add_argument(
          "--start_epoch",
          type=int,
          default=0,
          metavar="N",
          help="start epochs (default:0)",
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=16,
          metavar="N",
          help="input batch size for \
                          training (default: 16)",
      )
      parser.add_argument(
          "--test-batch-size",
          type=int,
          default=16,
          metavar="N",
          help="input batch size for \
                          testing (default: 16)",
      )
      parser.add_argument(
          "--optimizer",
          type=str,
          default="sgd",
          help="optimizer (default: sgd)",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=1e-3,
          metavar="LR",
          help="learning rate (default: 1e-3)",
      )
      parser.add_argument(
          "--warmup-epochs",
          type=int,
          default=0,
          help="number of warmup epochs.",
      )
      parser.add_argument(
          "--momentum",
          type=float,
          default=0.9,
          metavar="M",
          help="momentum (default: 0.9)",
      )
      parser.add_argument(
          "--weight-decay",
          type=float,
          default=1e-4,
          metavar="M",
          help="w-decay (default: 1e-4)",
      )
      parser.add_argument(
          "--no-wd",
          action="store_true",
          help="whether to remove weight decay on bias, \
                          and beta/gamma for batchnorm layers.",
      )
      parser.add_argument(
          "--mode",
          type=str,
          default=None,
          help="whether to turn on model hybridization",
      )
      # cuda and distribute
      parser.add_argument(
          "--no-cuda",
          action="store_true",
          default=False,
          help="disables CUDA training",
      )
      parser.add_argument(
          "--ngpus",
          type=int,
          default=len(mx.test_utils.list_gpus()),
          help="number of GPUs (default: 4)",
      )
      parser.add_argument(
          "--kvstore",
          type=str,
          default="device",
          help="kvstore to use for trainer/module.",
      )
      parser.add_argument(
          "--dtype",
          type=str,
          default="float32",
          help="data type for training. default is float32",
      )
      # checking point
      parser.add_argument(
          "--resume",
          type=str,
          default=None,
          help="put the path to resuming file if needed",
      )
      parser.add_argument(
          "--checkname",
          type=str,
          default="default",
          help="set the checkpoint name",
      )
      parser.add_argument(
          "--save-dir",
          type=str,
          default=None,
          help="directory of saved models",
      )
      parser.add_argument(
          "--log-interval",
          type=int,
          default=20,
          help="Number of batches to wait before logging.",
      )
      parser.add_argument(
          "--logging-file",
          type=str,
          default="train.log",
          help="name of training log file",
      )
      # evaluation only
      parser.add_argument(
          "--eval",
          action="store_true",
          default=False,
          help="evaluation only",
      )
      parser.add_argument(
          "--no-val",
          action="store_true",
          default=False,
          help="skip validation during training",
      )
      # synchronized Batch Normalization
      parser.add_argument(
          "--syncbn",
          action="store_true",
          default=False,
          help="using Synchronized Cross-GPU BatchNorm",
      )
      # the parser
      args = parser.parse_args()
  
      # handle contexts
      if args.no_cuda:
          print("Using CPU")
          args.kvstore = "local"
          args.ctx = [mx.cpu(0)]
      else:
          print("Number of GPUs:", args.ngpus)
          assert (
              args.ngpus > 0
          ), "No GPUs found, please enable --no-cuda for CPU mode."
          args.ctx = [mx.gpu(i) for i in range(args.ngpus)]
  
      if "psp" in args.model or "deeplab" in args.model:
          assert args.crop_size % 8 == 0, (
              "For PSPNet and DeepLabV3 model families, "
              "we only support input crop size as multiples of 8."
          )
  
      # logging and checkpoint saving
      if args.save_dir is None:
          args.save_dir = "runs/%s/%s/%s/" % (
              args.dataset,
              args.model,
              args.backbone,
          )
      makedirs(args.save_dir)
  
      # Synchronized BatchNorm
      args.norm_layer = (
          mx.gluon.contrib.nn.SyncBatchNorm
          if args.syncbn
          else mx.gluon.nn.BatchNorm
      )
      args.norm_kwargs = (
          {"num_devices": args.ngpus} if args.syncbn else {}
      )
      return args
  
imports: import os
  import logging
  import shutil
  import argparse
  import numpy
  from tqdm import tqdm
  import mxnet
  from mxnet import gluon, autograd
  from mxnet.gluon.data.vision import transforms
  import gluoncv
  from gluoncv.loss import *
  from gluoncv.utils import makedirs, LRScheduler, LRSequential
  from gluoncv.model_zoo.segbase import *
  from gluoncv.model_zoo import get_model
  from gluoncv.utils.parallel import *
  from gluoncv.data import get_segmentation_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args