code: def keypts_encoding(keypoints, num_classes):
      keypoints = tf.to_int32(keypoints)
      keypoints = tf.reshape(keypoints, (-1,))
      keypoints = slim.layers.one_hot_encoding(
          keypoints, num_classes=num_classes + 1
      )
      return keypoints
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  import menpo.io
  from menpo.image import Image
  from menpo.shape import PointCloud
  import cv2
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.contrib.framework.python.ops import variables
  from tensorflow.python.training import optimizer
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import variables
  from menpo.transform import Translation, Scale
  from menpo.shape import PointCloud
label_desc: Use image node_id 11 for calling a global function with example usage: keypts_encoding(keypoints, num_classes) and returns: keypoints