code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Train a model for image classification."
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=32,
          help="training batch size per device (CPU/GPU).",
      )
      parser.add_argument(
          "--num-gpus",
          type=int,
          default=0,
          help="number of gpus to use.",
      )
      parser.add_argument(
          "--model",
          type=str,
          default="resnet",
          help="model to use. options are resnet and wrn. default is resnet.",
      )
      parser.add_argument(
          "-j",
          "--num-data-workers",
          dest="num_workers",
          default=4,
          type=int,
          help="number of preprocessing workers",
      )
      parser.add_argument(
          "--num-epochs",
          type=int,
          default=200,
          help="number of training epochs.",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.1,
          help="learning rate. default is 0.1.",
      )
      parser.add_argument(
          "--momentum",
          type=float,
          default=0.9,
          help="momentum value for optimizer, default is 0.9.",
      )
      parser.add_argument(
          "--wd",
          type=float,
          default=0.0005,
          help="weight decay rate. default is 0.0005.",
      )
      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="period in epoch for learning rate decays. default is 0 (has no effect).",
      )
      parser.add_argument(
          "--lr-decay-epoch",
          type=str,
          default="100,150",
          help="epochs at which learning rate decays. default is 100,150.",
      )
      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(
          "--drop-rate",
          type=float,
          default=0.0,
          help="dropout rate for wide resnet. default is 0.",
      )
      parser.add_argument(
          "--mode",
          type=str,
          help="mode in which to train the model. options are imperative, hybrid",
      )
      parser.add_argument(
          "--save-period",
          type=int,
          default=10,
          help="period in epoch of model saving.",
      )
      parser.add_argument(
          "--save-dir",
          type=str,
          default="params",
          help="directory of saved models",
      )
      parser.add_argument(
          "--resume-from",
          type=str,
          help="resume training from the model",
      )
      parser.add_argument(
          "--logging-file",
          type=str,
          default="baseline",
          help="name of training log file",
      )
      # additional arguments for gradient compression
      parser.add_argument(
          "--compressor", type=str, default="", help="which compressor"
      )
      parser.add_argument(
          "--ef", type=str, default="", help="which error-feedback"
      )
      parser.add_argument(
          "--compress-momentum",
          type=str,
          default="",
          help="which compress momentum",
      )
      parser.add_argument(
          "--onebit-scaling",
          action="store_true",
          default=False,
          help="enable scaling for onebit compressor",
      )
      parser.add_argument(
          "--k", default=1.0, type=float, help="topk or randomk"
      )
      parser.add_argument(
          "--fp16-pushpull",
          action="store_true",
          default=False,
          help="use fp16 compression during pushpull",
      )
      opt = parser.parse_args()
      return opt
  
file_path: byteps/example/mxnet/train_cifar100_byteps_gc.py
imports: import argparse
  import logging
  import subprocess
  import time
  import gluoncv
  import matplotlib
  import mxnet
  from gluoncv.data import transforms
  from gluoncv.model_zoo import get_model
  from gluoncv.utils import LRScheduler, LRSequential, makedirs
  from mxnet import autograd
  from mxnet import gluon
  from mxnet.gluon.data.vision import transforms
  import byteps.mxnet
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: opt