code: def parse_args():
      parser = argparse.ArgumentParser(description="Eval SSD networks.")
      parser.add_argument(
          "--network",
          type=str,
          default="vgg16_atrous",
          help="Base network name",
      )
      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(
          "--data-shape", type=int, default=300, help="Input data shape"
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=64,
          help="eval mini-batch size",
      )
      parser.add_argument(
          "--benchmark",
          action="store_true",
          help="run dummy-data based benchmarking",
      )
      parser.add_argument(
          "--num-iterations",
          type=int,
          default=100,
          help="number of benchmarking iterations.",
      )
      parser.add_argument(
          "--dataset", type=str, default="voc", help="eval dataset."
      )
      parser.add_argument(
          "--num-workers",
          "-j",
          dest="num_workers",
          type=int,
          default=4,
          help="Number of data workers",
      )
      parser.add_argument(
          "--num-gpus",
          type=int,
          default=0,
          help="number of gpus to use.",
      )
      parser.add_argument(
          "--pretrained",
          type=str,
          default="True",
          help="Load weights from previously saved parameters.",
      )
      parser.add_argument(
          "--save-prefix",
          type=str,
          default="",
          help="Saving parameter prefix",
      )
      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.",
      )
      args = parser.parse_args()
      return args
  
imports: from __future__ import division
  from __future__ import print_function
  import argparse
  import logging
  import time
  import sys
  import numpy
  import mxnet
  from tqdm import tqdm
  from mxnet import nd
  from mxnet import gluon
  import gluoncv
  from gluoncv import data
  from gluoncv.data.batchify import Tuple, Stack, Pad
  from gluoncv.data.transforms.presets.ssd import SSDDefaultValTransform
  from gluoncv.utils.metrics.voc_detection import VOC07MApMetric
  from gluoncv.utils.metrics.coco_detection import COCODetectionMetric
  from mxnet.contrib.quantization import *
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args