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.
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docstr: :param clip_min: The minimum value to allow after normalizing the observation
  :param clip_max: The maximum value to allow after normalizing the observation
docstr_tok: [":","param","clip_min",":","The","minimum","value","to","allow","after","normalizing","the","observation",":","param","clip_max",":","The","maximum","value","to","allow","after","normalizing","the","observation"]
code: def __init__(
      self,
      clip_min: float = -5.0,
      clip_max: float = 5.0,
      name="observation_stats",
  ):
      """
      :param clip_min: The minimum value to allow after normalizing the observation
      :param clip_max: The maximum value to allow after normalizing the observation
      """
      super().__init__()
      self.clip_min = clip_min
      self.clip_max = clip_max
      self.running_observation_stats = None
      self.name = name
      self.supports_batching = True
      self.observation_space = None
  
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 1 to create a new ObservationNormalizationFilter object from inherited base classes: ObservationFilter with example: obj = ObservationNormalizationFilter(clip_min, clip_max, name)