class_docstr: Masks part of the action space to enforce the agent to work in a defined space. For example,
  if the original action space is between -1 and 1, then this filter can be used in order to constrain the agent actions
  to the range 0 and 1 instead. This essentially masks the range -1 and 0 from the agent.
  The resulting action space will be shifted and will always start from 0 and have the size of the unmasked area.
code: def get_unfiltered_action_space(
      self, output_action_space: BoxActionSpace
  ) -> BoxActionSpace:
      self.output_action_space = output_action_space
      self.input_action_space = BoxActionSpace(
          output_action_space.shape,
          low=0,
          high=self.masked_target_space_high
          - self.masked_target_space_low,
      )
      return self.input_action_space
  
file_path: coach/rl_coach/filters/action/box_masking.py
imports: from typing import Union
  import numpy
  from rl_coach.core_types import ActionType
  from rl_coach.filters.action.action_filter import ActionFilter
  from rl_coach.spaces import BoxActionSpace
label_desc: Use image node_id 4 for calling the BoxMasking obj's underlying member method code with example usage: obj.get_unfiltered_action_space(output_action_space) and returns: self