code: def reward_normalize(reward):
      reward = np.asarray(reward)
      max_r = np.max(reward)
      min_r = np.min(reward)
      if max_r == min_r:
          reward = np.zeros(reward.shape)
      else:
          reward = (reward - min_r) / (max_r - min_r)
          reward -= 0.5
      return reward
  
file_path: PARL/examples/others/deepes.py
imports: import gym
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: reward_normalize(reward) and returns: reward