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.
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docstr: This wrapper will normalize observations s.t. each coordinate is centered with unit variance.
  
  Args:
      env (Env): The environment to apply the wrapper
      epsilon: A stability parameter that is used when scaling the observations.
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code: def __init__(self, env: gym.Env, epsilon: float = 1e-8):
      """This wrapper will normalize observations s.t. each coordinate is centered with unit variance.
  
      Args:
          env (Env): The environment to apply the wrapper
          epsilon: A stability parameter that is used when scaling the observations.
      """
      super().__init__(env)
      self.num_envs = getattr(env, "num_envs", 1)
      self.is_vector_env = getattr(env, "is_vector_env", False)
      if self.is_vector_env:
          self.obs_rms = RunningMeanStd(
              shape=self.single_observation_space.shape
          )
      else:
          self.obs_rms = RunningMeanStd(
              shape=self.observation_space.shape
          )
      self.epsilon = epsilon
  
file_path: gym/gym/wrappers/normalize.py
imports: import numpy
  import gym
label_desc: Use image node_id 1 to create a new NormalizeObservation object from inherited base classes: gym.core with example: obj = NormalizeObservation(env, epsilon)