code: def darknet_parse(architecture_name, test_input_path):
      ensure_dir("./data/")
      from mmdnn.conversion.examples.darknet.extractor import (
          darknet_extractor,
      )
      from mmdnn.conversion.darknet.darknet_parser import DarknetParser
  
      # download model
      architecture_file = darknet_extractor.download(
          architecture_name, TestModels.cachedir
      )
  
      # get original model prediction result
      original_predict = darknet_extractor.inference(
          architecture_name,
          architecture_file,
          TestModels.cachedir,
          test_input_path,
      )
      del darknet_extractor
  
      # original to IR
      IR_file = (
          TestModels.tmpdir
          + "darknet_"
          + architecture_name
          + "_converted"
      )
  
      if architecture_name == "yolov3":
          start = "1"
      else:
          start = "0"
  
      parser = DarknetParser(
          architecture_file[0], architecture_file[1], start
      )
      parser.run(IR_file)
      del parser
      del DarknetParser
      return original_predict
  
imports: from __future__ import absolute_import
  from __future__ import print_function
  import os
  import sys
  import imp
  import numpy
  from mmdnn.conversion.examples.imagenet_test import TestKit
  import utils
  from utils import *
  from datetime import datetime
label_desc: Use image node_id 11 for calling the TestModels obj's underlying member method code with example usage: obj.darknet_parse(architecture_name, test_input_path) and returns: original_predict