code: def tensorflow_parse(architecture_name, test_input_path):
      from mmdnn.conversion.examples.tensorflow.extractor import (
          tensorflow_extractor,
      )
      from mmdnn.conversion.tensorflow.tensorflow_parser import (
          TensorflowParser,
      )
  
      # get original model prediction result
      original_predict = tensorflow_extractor.inference(
          architecture_name, None, TestModels.cachedir, test_input_path
      )
      del tensorflow_extractor
  
      # original to IR
      IR_file = (
          TestModels.tmpdir
          + "tensorflow_"
          + architecture_name
          + "_converted"
      )
      parser = TensorflowParser(
          TestModels.cachedir
          + "imagenet_"
          + architecture_name
          + ".ckpt.meta",
          TestModels.cachedir
          + "imagenet_"
          + architecture_name
          + ".ckpt",
          ["MMdnn_Output"],
      )
      parser.run(IR_file)
      del parser
      del TensorflowParser
  
      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 2 for calling the TestModels obj's underlying member method code with example usage: obj.tensorflow_parse(architecture_name, test_input_path) and returns: original_predict