code: def mean_absolute_percentage_error(y_true, y_pred):
      """Mean absolute percentage error loss.
  
      Args:
        y_true: tf.Tensor.
        y_pred: tf.Tensor.
          Tensors of same shape and type.
      """
      diff = tf.abs(
          (y_true - y_pred)
          / tf.clip_by_value(tf.abs(y_true), 1e-8, np.inf)
      )
      return 100.0 * tf.reduce_mean(diff, axis=-2)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import six
  import tensorflow
  from edward.models import RandomVariable
  from edward.util import check_data, get_session, compute_multinomial_mode, with_binary_averaging
label_desc: Use image node_id 14 for calling a global function with example usage: mean_absolute_percentage_error(y_true, y_pred) and returns: unknown