class_docstr: This wrapper will normalize immediate rewards s.t. their exponential moving average has a fixed variance.
  
  The exponential moving average will have variance :math:`(1 - \gamma)^2`.
  
  Note:
      The scaling depends on past trajectories and rewards will not be scaled correctly if the wrapper was newly
      instantiated or the policy was changed recently.
class_docstr_tok: ["This","wrapper","will","normalize","immediate","rewards","s.t",".","their","exponential","moving","average","has","a","fixed","variance",".","The","exponential","moving","average","will","have","variance",":","math",":","`","(","1","-","\\gamma",")","^2","`",".","Note",":","The","scaling","depends","on","past","trajectories","and","rewards","will","not","be","scaled","correctly","if","the","wrapper","was","newly","instantiated","or","the","policy","was","changed","recently","."]
docstr: Normalizes the rewards with the running mean rewards and their variance.
docstr_tok: ["Normalizes","the","rewards","with","the","running","mean","rewards","and","their","variance","."]
code: def normalize(self, rews):
      """Normalizes the rewards with the running mean rewards and their variance."""
      self.return_rms.update(self.returns)
      return rews / np.sqrt(self.return_rms.var + self.epsilon)
  
file_path: gym/gym/wrappers/normalize.py
imports: import numpy
  import gym
label_desc: Use image node_id 3 for calling the NormalizeReward obj's underlying member method code with example usage: obj.normalize(rews) and returns: unknown