docs: def _parse_example_proto(example_serialized):
    """Parses an Example proto containing a training example of an image.
  
    The output of the build_image_data.py image preprocessing script is a dataset
    containing serialized Example protocol buffers. Each Example proto contains
    the following fields (values are included as examples):
  
      image/height: 462
      image/width: 581
      image/colorspace: 'RGB'
      image/channels: 3
      image/class/label: 615
      image/class/synset: 'n03623198'
      image/class/text: 'knee pad'
      image/object/bbox/xmin: 0.1
      image/object/bbox/xmax: 0.9
      image/object/bbox/ymin: 0.2
      image/object/bbox/ymax: 0.6
      image/object/bbox/label: 615
      image/format: 'JPEG'
      image/filename: 'ILSVRC2012_val_00041207.JPEG'
      image/encoded: <JPEG encoded string>
  
    Args:
      example_serialized: scalar Tensor tf.string containing a serialized
        Example protocol buffer.
  
    Returns:
      image_buffer: Tensor tf.string containing the contents of a JPEG file.
      label: Tensor tf.int32 containing the label.
      bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
        where each coordinate is [0, 1) and the coordinates are arranged as
        [ymin, xmin, ymax, xmax].
    """
    # Dense features in Example proto.
    feature_map = {
        'image/encoded': tf.FixedLenFeature([], dtype=tf.string,
                                            default_value=''),
        'image/class/label': tf.FixedLenFeature([], dtype=tf.int64,
                                                default_value=-1),
        'image/class/text': tf.FixedLenFeature([], dtype=tf.string,
                                               default_value=''),
    }
    sparse_float32 = tf.VarLenFeature(dtype=tf.float32)
    # Sparse features in Example proto.
    feature_map.update(
        {k: sparse_float32 for k in ['image/object/bbox/xmin',
                                     'image/object/bbox/ymin',
                                     'image/object/bbox/xmax',
                                     'image/object/bbox/ymax']})
  
    features = tf.parse_single_example(example_serialized, feature_map)
    label = tf.cast(features['image/class/label'], dtype=tf.int32)
  
    return features['image/encoded'], label