code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Train YOLO networks with random input shape."
      )
      parser.add_argument(
          "--network",
          type=str,
          default="darknet53",
          help="Base network name which serves as feature extraction base.",
      )
      parser.add_argument(
          "--data-shape",
          type=int,
          default=416,
          help="Input data shape for evaluation, use 320, 416, 608... "
          + "Training is with random shapes from (320 to 608).",
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=64,
          help="Training mini-batch size",
      )
      parser.add_argument(
          "--dataset",
          type=str,
          default="voc",
          help="Training dataset. Now support voc.",
      )
      parser.add_argument(
          "--num-workers",
          "-j",
          dest="num_workers",
          type=int,
          default=4,
          help="Number of data workers, you can use larger "
          "number to accelerate data loading, if you CPU and GPUs are powerful.",
      )
      parser.add_argument(
          "--gpus",
          type=str,
          default="0",
          help="Training with GPUs, you can specify 1,3 for example.",
      )
      parser.add_argument(
          "--epochs", type=int, default=200, help="Training epochs."
      )
      parser.add_argument(
          "--resume",
          type=str,
          default="",
          help="Resume from previously saved parameters if not None. "
          "For example, you can resume from ./yolo3_xxx_0123.params",
      )
      parser.add_argument(
          "--start-epoch",
          type=int,
          default=0,
          help="Starting epoch for resuming, default is 0 for new training."
          "You can specify it to 100 for example to start from 100 epoch.",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.001,
          help="Learning rate, default is 0.001",
      )
      parser.add_argument(
          "--lr-mode",
          type=str,
          default="step",
          help="learning rate scheduler mode. options are step, poly and cosine.",
      )
      parser.add_argument(
          "--lr-decay",
          type=float,
          default=0.1,
          help="decay rate of learning rate. default is 0.1.",
      )
      parser.add_argument(
          "--lr-decay-period",
          type=int,
          default=0,
          help="interval for periodic learning rate decays. default is 0 to disable.",
      )
      parser.add_argument(
          "--lr-decay-epoch",
          type=str,
          default="160,180",
          help="epochs at which learning rate decays. default is 160,180.",
      )
      parser.add_argument(
          "--warmup-lr",
          type=float,
          default=0.0,
          help="starting warmup learning rate. default is 0.0.",
      )
      parser.add_argument(
          "--warmup-epochs",
          type=int,
          default=0,
          help="number of warmup epochs.",
      )
      parser.add_argument(
          "--momentum",
          type=float,
          default=0.9,
          help="SGD momentum, default is 0.9",
      )
      parser.add_argument(
          "--wd",
          type=float,
          default=0.0005,
          help="Weight decay, default is 5e-4",
      )
      parser.add_argument(
          "--log-interval",
          type=int,
          default=100,
          help="Logging mini-batch interval. Default is 100.",
      )
      parser.add_argument(
          "--save-prefix",
          type=str,
          default="",
          help="Saving parameter prefix",
      )
      parser.add_argument(
          "--save-interval",
          type=int,
          default=10,
          help="Saving parameters epoch interval, best model will always be saved.",
      )
      parser.add_argument(
          "--val-interval",
          type=int,
          default=1,
          help="Epoch interval for validation, increase the number will reduce the "
          "training time if validation is slow.",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=233,
          help="Random seed to be fixed.",
      )
      parser.add_argument(
          "--num-samples",
          type=int,
          default=-1,
          help="Training images. Use -1 to automatically get the number.",
      )
      parser.add_argument(
          "--syncbn",
          action="store_true",
          help="Use synchronize BN across devices.",
      )
      parser.add_argument(
          "--no-random-shape",
          action="store_true",
          help="Use fixed size(data-shape) throughout the training, which will be faster "
          "and require less memory. However, final model will be slightly worse.",
      )
      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(
          "--mixup",
          action="store_true",
          help="whether to enable mixup.",
      )
      parser.add_argument(
          "--no-mixup-epochs",
          type=int,
          default=20,
          help="Disable mixup training if enabled in the last N epochs.",
      )
      parser.add_argument(
          "--label-smooth",
          action="store_true",
          help="Use label smoothing.",
      )
      parser.add_argument(
          "--amp",
          action="store_true",
          help="Use MXNet AMP for mixed precision training.",
      )
      parser.add_argument(
          "--horovod",
          action="store_true",
          help="Use MXNet Horovod for distributed training. Must be run with OpenMPI. "
          "--gpus is ignored when using --horovod.",
      )
  
      args = parser.parse_args()
      return args
  
imports: import argparse
  import os
  import logging
  import time
  import warnings
  import numpy
  import mxnet
  from mxnet import nd
  from mxnet import gluon
  from mxnet import autograd
  import gluoncv
  from gluoncv import data
  from gluoncv import utils
  from gluoncv.model_zoo import get_model
  from gluoncv.data.batchify import Tuple, Stack, Pad
  from gluoncv.data.transforms.presets.yolo import YOLO3DefaultTrainTransform
  from gluoncv.data.transforms.presets.yolo import YOLO3DefaultValTransform
  from gluoncv.data.dataloader import RandomTransformDataLoader
  from gluoncv.utils.metrics.voc_detection import VOC07MApMetric
  from gluoncv.utils.metrics.coco_detection import COCODetectionMetric
  from gluoncv.utils import LRScheduler, LRSequential
  from mxnet.contrib import amp
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args