code: def mean_q(y_true, y_pred):
      return K.mean(K.max(y_pred, axis=-1))
  
imports: from __future__ import division
  from collections import deque
  import os
  import warnings
  import numpy
  import keras.backend
  import keras.optimizers
  from rl.core import Agent
  from rl.random import OrnsteinUhlenbeckProcess
  from rl.util import *
label_desc: Use image node_id 1 for calling a global function with example usage: mean_q(y_true, y_pred) and returns: K