code: def handle_checkpoint(cls, architecture, path):
      with slim.arg_scope(
          cls.architecture_map[architecture]["arg_scope"]()
      ):
          data_input = cls.architecture_map[architecture]["input"]()
          logits, endpoints = cls.architecture_map[architecture][
              "builder"
          ]()(
              data_input,
              num_classes=cls.architecture_map[architecture][
                  "num_classes"
              ],
              is_training=False,
          )
  
          if logits.op.type == "Squeeze":
              labels = tf.identity(logits, name="MMdnn_Output")
          else:
              labels = tf.squeeze(logits, name="MMdnn_Output")
  
      init = tf.global_variables_initializer()
      with tf.Session() as sess:
          sess.run(init)
          saver = tf.train.Saver()
          saver.restore(
              sess,
              path + cls.architecture_map[architecture]["filename"],
          )
          save_path = saver.save(
              sess, path + "imagenet_{}.ckpt".format(architecture)
          )
          print("Model saved in file: %s" % save_path)
  
      import tensorflow.contrib.keras as keras
  
      keras.backend.clear_session()
  
imports: from __future__ import absolute_import
  import os
  import tensorflow
  from tensorflow.contrib.slim.nets import vgg
  from tensorflow.contrib.slim.nets import inception
  from tensorflow.contrib.slim.nets import resnet_v1
  from tensorflow.contrib.slim.nets import resnet_v2
  from mmdnn.conversion.examples.tensorflow.models import inception_resnet_v2
  from mmdnn.conversion.examples.tensorflow.models import mobilenet_v1
  from mmdnn.conversion.examples.tensorflow.models import nasnet
  from mmdnn.conversion.examples.tensorflow.models.mobilenet import mobilenet_v2
  from mmdnn.conversion.examples.tensorflow.models import inception_resnet_v1
  from mmdnn.conversion.examples.tensorflow.models import test_rnn
  from mmdnn.conversion.examples.imagenet_test import TestKit
  from mmdnn.conversion.examples.extractor import base_extractor
  from mmdnn.conversion.common.utils import download_file
label_desc: Use image node_id 1 for calling the tensorflow_extractor obj's underlying member method code with example usage: obj.handle_checkpoint(cls, architecture, path) without return types