class_docstr: An environment wrapper for normalization.
  
  This wrapper normalizes action, and optionally observation and reward.
  
  Args:
      env (Environment): An environment instance.
      scale_reward (float): Scale of environment reward.
      normalize_obs (bool): If True, normalize observation.
      normalize_reward (bool): If True, normalize reward. scale_reward is
          applied after normalization.
      expected_action_scale (float): Assuming action falls in the range of
          [-expected_action_scale, expected_action_scale] when normalize it.
      flatten_obs (bool): Flatten observation if True.
      obs_alpha (float): Update rate of moving average when estimating the
          mean and variance of observations.
      reward_alpha (float): Update rate of moving average when estimating the
          mean and variance of rewards.
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docstr: Call reset on wrapped env.
  
  Returns:
      numpy.ndarray: The first observation conforming to
          `observation_space`.
      dict: The episode-level information.
          Note that this is not part of `env_info` provided in `step()`.
          It contains information of he entire episode， which could be
          needed to determine the first action (e.g. in the case of
          goal-conditioned or MTRL.)
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code: def reset(self):
      """Call reset on wrapped env.
  
      Returns:
          numpy.ndarray: The first observation conforming to
              `observation_space`.
          dict: The episode-level information.
              Note that this is not part of `env_info` provided in `step()`.
              It contains information of he entire episode， which could be
              needed to determine the first action (e.g. in the case of
              goal-conditioned or MTRL.)
  
      """
      first_obs, episode_info = self._env.reset()
      if self._normalize_obs:
          return self._apply_normalize_obs(first_obs), episode_info
      else:
          return first_obs, episode_info
  
file_path: garage/src/garage/envs/normalized_env.py
imports: import akro
  import numpy
  from garage import EnvStep, Wrapper
label_desc: Use image node_id 2 for calling the NormalizedEnv obj's underlying member method code with example usage: obj.reset() and returns: self,  episode_info, first_obs,  episode_info