class_docstr: Normalizes the reward values based on Exponential Weighted Moving Average.   
code: def get_filtered_reward_space(
      self, input_reward_space: RewardSpace
  ) -> RewardSpace:
      return input_reward_space
  
file_path: coach/rl_coach/filters/reward/reward_ewma_normalization_filter.py
imports: import os
  import numpy
  import pickle
  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.utils import get_latest_checkpoint
label_desc: Use image node_id 3 for calling the RewardEwmaNormalizationFilter obj's underlying member method code with example usage: obj.get_filtered_reward_space(input_reward_space) and returns: input_reward_space