class_docstr: Clips the observation values to a given range of values.
  For example, if the observation consists of measurements in an arbitrary range,
  and we want to control the minimum and maximum values of these observations,
  we can define a range and clip the values of the measurements.
docstr: :param clipping_low: The minimum value to allow after normalizing the observation
  :param clipping_high: The maximum value to allow after normalizing the observation
docstr_tok: [":","param","clipping_low",":","The","minimum","value","to","allow","after","normalizing","the","observation",":","param","clipping_high",":","The","maximum","value","to","allow","after","normalizing","the","observation"]
code: def __init__(
      self, clipping_low: float = -np.inf, clipping_high: float = np.inf
  ):
      """
      :param clipping_low: The minimum value to allow after normalizing the observation
      :param clipping_high: The maximum value to allow after normalizing the observation
      """
      super().__init__()
      self.clip_min = clipping_low
      self.clip_max = clipping_high
  
file_path: coach/rl_coach/filters/observation/observation_clipping_filter.py
imports: import numpy
  from rl_coach.core_types import ObservationType
  from rl_coach.filters.observation.observation_filter import ObservationFilter
  from rl_coach.spaces import ObservationSpace
label_desc: Use image node_id 1 to create a new ObservationClippingFilter object from inherited base classes: ObservationFilter with example: obj = ObservationClippingFilter(clipping_low, clipping_high)