code: def tensorflow_emit(
      original_framework,
      architecture_name,
      architecture_path,
      weight_path,
      test_input_path,
  ):
      import tensorflow as tf
      from mmdnn.conversion.tensorflow.tensorflow_emitter import (
          TensorflowEmitter,
      )
  
      # IR to code
      converted_file = (
          TestModels.tmpdir
          + original_framework
          + "_tensorflow_"
          + architecture_name
          + "_converted"
      )
      converted_file = converted_file.replace(".", "_")
  
      emitter = TensorflowEmitter((architecture_path, weight_path))
      emitter.run(converted_file + ".py", None, "test")
      del emitter
      del TensorflowEmitter
  
      # import converted model
      model_converted = imp.load_source(
          "TFModel", converted_file + ".py"
      ).KitModel(weight_path)
  
      input_tf, model_tf = model_converted
  
      original_framework = checkfrozen(original_framework)
  
      if "rnn" not in architecture_name:
          func = TestKit.preprocess_func[original_framework][
              architecture_name
          ]
          img = func(test_input_path)
          input_data = np.expand_dims(img, 0)
      else:
          input_data = np.load(test_input_path)
  
      with tf.Session() as sess:
          init = tf.global_variables_initializer()
          sess.run(init)
          predict = sess.run(model_tf, feed_dict={input_tf: input_data})
      del model_converted
      del sys.modules["TFModel"]
      os.remove(converted_file + ".py")
      converted_predict = np.squeeze(predict)
  
      del tf
  
      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 13 for calling the TestModels obj's underlying member method code with example usage: obj.tensorflow_emit(original_framework, architecture_name, architecture_path, weight_path, test_input_path) and returns: converted_predict