def METHOD_NAME(example_serialized):
  """Parses an Example proto containing a training example of an image.
  """
  # Dense features in Example proto.
  feature_map = {
    'image/encoded': tf.io.FixedLenFeature([], dtype=tf.string,
                                        default_value=''),
    'image/class/label': tf.io.FixedLenFeature([1], dtype=tf.int64,
                                            default_value=-1),
    'image/filename': tf.io.FixedLenFeature([], dtype=tf.string,
                                         default_value="")
  }
  sparse_float32 = tf.io.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.io.parse_single_example(serialized=example_serialized, features=feature_map)
  label = tf.cast(features['image/class/label'], dtype=tf.int32)
  filename = tf.cast(features['image/filename'], dtype=tf.string)
  return features['image/encoded'], label, filename