code: def cntk_emit(
      original_framework,
      architecture_name,
      architecture_path,
      weight_path,
      test_input_path,
  ):
      from mmdnn.conversion.cntk.cntk_emitter import CntkEmitter
  
      # IR to code
      converted_file = (
          TestModels.tmpdir
          + original_framework
          + "_cntk_"
          + architecture_name
          + "_converted"
      )
      converted_file = converted_file.replace(".", "_")
      emitter = CntkEmitter((architecture_path, weight_path))
      emitter.run(converted_file + ".py", None, "test")
      del emitter
      del CntkEmitter
  
      model_converted = imp.load_source(
          "CntkModel", converted_file + ".py"
      ).KitModel(weight_path)
  
      if "rnn" not in architecture_name:
          func = TestKit.preprocess_func[original_framework][
              architecture_name
          ]
          img = func(test_input_path)
          input_data = img
      else:
          sentence = np.load(test_input_path)
          from keras.utils import to_categorical
  
          input_data = to_categorical(sentence, 30000)[0]
  
      predict = model_converted.eval(
          {model_converted.arguments[0]: [input_data]}
      )
      converted_predict = np.squeeze(predict)
      del model_converted
      del sys.modules["CntkModel"]
      os.remove(converted_file + ".py")
  
      return converted_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 12 for calling the TestModels obj's underlying member method code with example usage: obj.cntk_emit(original_framework, architecture_name, architecture_path, weight_path, test_input_path) and returns: converted_predict