code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Train a model for image classification."
      )
      parser.add_argument(
          "--data-dir",
          type=str,
          default="~/.mxnet/datasets/imagenet",
          help="Imagenet directory for validation.",
      )
      parser.add_argument(
          "--rec-dir",
          type=str,
          default="",
          help="recio directory for validation.",
      )
      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(
          "-j",
          "--num-data-workers",
          dest="num_workers",
          default=4,
          type=int,
          help="number of preprocessing workers",
      )
      parser.add_argument(
          "--model",
          type=str,
          default="model",
          required=False,
          help="type of model to use. see vision_model for options.",
      )
      parser.add_argument(
          "--deploy",
          action="store_true",
          help="whether load static model for deployment",
      )
      parser.add_argument(
          "--model-prefix",
          type=str,
          required=False,
          help="load static model as hybridblock.",
      )
      parser.add_argument(
          "--quantized",
          action="store_true",
          help="use int8 pretrained model",
      )
      parser.add_argument(
          "--input-size",
          type=int,
          default=224,
          help="input shape of the image, default is 224.",
      )
      parser.add_argument(
          "--num-batches",
          type=int,
          default=100,
          help="run specified number of batches for inference",
      )
      parser.add_argument(
          "--benchmark",
          action="store_true",
          help="use synthetic data to evalute benchmark",
      )
      parser.add_argument(
          "--crop-ratio",
          type=float,
          default=0.875,
          help="The ratio for crop and input size, for validation dataset only",
      )
      parser.add_argument(
          "--params-file",
          type=str,
          help="local parameter file to load, instead of pre-trained weight.",
      )
      parser.add_argument(
          "--dtype", type=str, help="training data type"
      )
      parser.add_argument(
          "--use_se",
          action="store_true",
          help="use SE layers or not in resnext. default is false.",
      )
      parser.add_argument(
          "--calibration", action="store_true", help="quantize model"
      )
      parser.add_argument(
          "--num-calib-batches",
          type=int,
          default=5,
          help="number of batches for calibration",
      )
      parser.add_argument(
          "--quantized-dtype",
          type=str,
          default="auto",
          choices=["auto", "int8", "uint8"],
          help="quantization destination data type for input data",
      )
      parser.add_argument(
          "--calib-mode",
          type=str,
          default="naive",
          help="calibration mode used for generating calibration table for the quantized symbol; supports"
          " 1. none: no calibration will be used. The thresholds for quantization will be calculated"
          " on the fly. This will result in inference speed slowdown and loss of accuracy"
          " in general."
          " 2. naive: simply take min and max values of layer outputs as thresholds for"
          " quantization. In general, the inference accuracy worsens with more examples used in"
          " calibration. It is recommended to use `entropy` mode as it produces more accurate"
          " inference results."
          " 3. entropy: calculate KL divergence of the fp32 output and quantized output for optimal"
          " thresholds. This mode is expected to produce the best inference accuracy of all three"
          " kinds of quantized models if the calibration dataset is representative enough of the"
          " inference dataset.",
      )
      opt = parser.parse_args()
      return opt
  
file_path: gluon-cv/scripts/classification/imagenet/verify_pretrained.py
imports: import argparse, os, math, time, sys
  import mxnet
  import logging
  from mxnet import gluon, nd, image
  from mxnet.gluon.nn import Block, HybridBlock
  from mxnet.gluon.data.vision import transforms
  from mxnet.contrib.quantization import *
  import gluoncv
  from gluoncv.data import imagenet
  from gluoncv.model_zoo import get_model
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: opt