code: def _parse_tfex_proto(example_proto):
      """Parses the tfexample proto to a raw mesh_data dictionary.
  
      Args:
        example_proto: A tf.Example proto storing the encoded mesh data.
  
      Returns:
        A mesh data dictionary with the following fields:
          'num_vertices': The `int64` number of vertices in mesh.
          'num_triangles': The `int64` number of triangles in mesh.
          'vertices': A serialized tensor of vertex positions.
          'triangles': A serialized tensor of triangle vertex indices.
          'labels': A serialized tensor of per vertex class labels.
      """
      feature_description = {
          "num_vertices": tf.io.FixedLenFeature(
              [], tf.int64, default_value=0
          ),
          "num_triangles": tf.io.FixedLenFeature(
              [], tf.int64, default_value=0
          ),
          "vertices": tf.io.FixedLenFeature(
              [], tf.string, default_value=""
          ),
          "triangles": tf.io.FixedLenFeature(
              [], tf.string, default_value=""
          ),
          "labels": tf.io.FixedLenFeature(
              [], tf.string, default_value=""
          ),
      }
      return tf.io.parse_single_example(
          serialized=example_proto, features=feature_description
      )
  
file_path: graphics/tensorflow_graphics/notebooks/mesh_segmentation_dataio.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Any, Callable, Dict, List, Tuple
  import numpy
  import tensorflow
  from tensorflow_graphics.geometry.convolution import utils
  from tensorflow_graphics.geometry.representation.mesh import utils
  from tensorflow_graphics.util import shape
  from tensorflow_graphics.util import type_alias
label_desc: Use image node_id 4 for calling a global function with example usage: _parse_tfex_proto(example_proto) and returns: tf