class_docstr: This wrapper will normalize observations s.t. each coordinate is centered with unit variance.
  
  Note:
      The normalization depends on past trajectories and observations will not be normalized correctly if the wrapper was
      newly instantiated or the policy was changed recently.
class_docstr_tok: ["This","wrapper","will","normalize","observations","s.t",".","each","coordinate","is","centered","with","unit","variance",".","Note",":","The","normalization","depends","on","past","trajectories","and","observations","will","not","be","normalized","correctly","if","the","wrapper","was","newly","instantiated","or","the","policy","was","changed","recently","."]
docstr: Normalises the observation using the running mean and variance of the observations.
docstr_tok: ["Normalises","the","observation","using","the","running","mean","and","variance","of","the","observations","."]
code: def normalize(self, obs):
      """Normalises the observation using the running mean and variance of the observations."""
      self.obs_rms.update(obs)
      return (obs - self.obs_rms.mean) / np.sqrt(
          self.obs_rms.var + self.epsilon
      )
  
file_path: gym/gym/wrappers/normalize.py
imports: import numpy
  import gym
label_desc: Use image node_id 4 for calling the NormalizeObservation obj's underlying member method code with example usage: obj.normalize(obs) and returns: unknown