class_docstr: Normalizes the reward values with a running mean and standard deviation of
  all the rewards seen so far. When working with multiple workers, the statistics used for the normalization operation
  are accumulated over all the workers.
class_docstr_tok: ["Normalizes","the","reward","values","with","a","running","mean","and","standard","deviation","of","all","the","rewards","seen","so","far",".","When","working","with","multiple","workers",",","the","statistics","used","for","the","normalization","operation","are","accumulated","over","all","the","workers","."]
code: def get_filtered_reward_space(
      self, input_reward_space: RewardSpace
  ) -> RewardSpace:
      self.running_rewards_stats.set_params(
          shape=(1,), clip_values=(self.clip_min, self.clip_max)
      )
      return input_reward_space
  
file_path: coach/rl_coach/filters/reward/reward_normalization_filter.py
imports: import os
  import numpy
  from rl_coach.core_types import RewardType
  from rl_coach.filters.reward.reward_filter import RewardFilter
  from rl_coach.spaces import RewardSpace
  from rl_coach.utilities.shared_running_stats import NumpySharedRunningStats
label_desc: Use image node_id 5 for calling the RewardNormalizationFilter obj's underlying member method code with example usage: obj.get_filtered_reward_space(input_reward_space) and returns: input_reward_space