code: def __init__(self, sess, model_saved_path, labels_path):
      self.image_value_range = (-1, 1)
      image_shape_v2 = [224, 224, 3]
      endpoints_v2 = dict(
          input="input:0",
          prediction="MobilenetV2/Predictions/Reshape:0",
      )
  
      self.sess = sess
      super(MobilenetV2Wrapper_public, self).__init__(
          sess,
          model_saved_path,
          labels_path,
          image_shape_v2,
          endpoints_v2,
          scope="MobilenetV2",
      )
  
      # define bottleneck tensors and their gradients
      self.bottlenecks_tensors = self.get_bottleneck_tensors_mobilenet(
          scope="MobilenetV2"
      )
      # Construct gradient ops.
      g = tf.compat.v1.get_default_graph()
      self._make_gradient_tensors()
      self.model_name = "MobilenetV2_public"
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from abc import ABCMeta
  from abc import abstractmethod
  from six.moves import zip
  import numpy
  import six
  import tensorflow
  from google.protobuf import text_format
label_desc: Use image node_id 1 to create a new MobilenetV2Wrapper_public object from inherited base classes: PublicImageModelWrapper with example: obj = MobilenetV2Wrapper_public(sess, model_saved_path, labels_path)