class_docstr: Normalizes the observation values with a running mean and standard deviation of
  all the observations seen so far. The normalization is performed element-wise. Additionally, when working with
  multiple workers, the statistics used for the normalization operation are accumulated over all the workers.
class_docstr_tok: ["Normalizes","the","observation","values","with","a","running","mean","and","standard","deviation","of","all","the","observations","seen","so","far",".","The","normalization","is","performed","element-wise",".","Additionally",",","when","working","with","multiple","workers",",","the","statistics","used","for","the","normalization","operation","are","accumulated","over","all","the","workers","."]
code: def get_filtered_observation_space(
      self, input_observation_space: ObservationSpace
  ) -> ObservationSpace:
      self.running_observation_stats.set_params(
          shape=input_observation_space.shape,
          clip_values=(self.clip_min, self.clip_max),
      )
      return input_observation_space
  
file_path: coach/rl_coach/filters/observation/observation_normalization_filter.py
imports: import os
  import pickle
  from typing import List
  import numpy
  from rl_coach.core_types import ObservationType
  from rl_coach.filters.observation.observation_filter import ObservationFilter
  from rl_coach.spaces import ObservationSpace
  from rl_coach.utilities.shared_running_stats import NumpySharedRunningStats, NumpySharedRunningStats
label_desc: Use image node_id 5 for calling the ObservationNormalizationFilter obj's underlying member method code with example usage: obj.get_filtered_observation_space(input_observation_space) and returns: input_observation_space