code: def _parse_mesh_data(mesh_data, mean_center=True):
      """Parses a raw mesh_data dictionary read from tf examples.
  
      Args:
        mesh_data: A mesh data dictionary with serialized data tensors,
          as output from _parse_tfex_proto()
        mean_center: If true, centers the mesh vertices to mean(vertices).
      Returns:
         A mesh data dictionary with following fields:
          'num_vertices': The `int32` number of vertices in mesh.
          'num_triangles': The `int32` number of triangles in mesh.
          'num_edges': The `int32` number of unique directed edges in mesh.
          'vertices': A [V, 3] `float32` of vertex positions.
          'triangles': A [T, 3] `int32` tensor of triangle vertex indices.
          'labels': A [V] `int32` tensor of per vertex class labels.
          'edges': A [E, 2] `int32` tensor of unique directed edges in mesh.
          'edge_weights': A [E] `float32` tensor of vertex degree based edge
            weights.
      """
      labels = tf.io.parse_tensor(mesh_data["labels"], tf.int32)
      vertices = tf.io.parse_tensor(mesh_data["vertices"], tf.float32)
      triangles = tf.io.parse_tensor(mesh_data["triangles"], tf.int32)
      if mean_center:
          vertices = vertices - tf.reduce_mean(
              input_tensor=vertices, axis=0, keepdims=True
          )
  
      edges, weights = tf.py_function(
          func=lambda t: get_weighted_edges(t.numpy()),
          inp=[triangles],
          Tout=[tf.int32, tf.float32],
      )
  
      num_edges = tf.shape(input=edges)[0]
      num_vertices = tf.cast(mesh_data["num_vertices"], tf.int32)
      num_triangles = tf.cast(mesh_data["num_triangles"], tf.int32)
      mesh_data = dict(
          vertices=vertices,
          labels=labels,
          triangles=triangles,
          edges=edges,
          edge_weights=weights,
          num_triangles=num_triangles,
          num_vertices=num_vertices,
          num_edges=num_edges,
      )
      return mesh_data
  
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 5 for calling a global function with example usage: _parse_mesh_data(mesh_data, mean_center) and returns: mesh_data