code: def test_get_filtered_reward_space():
      rescale_filter = InputFilter(
          reward_filters=OrderedDict(
              [("rescale", RewardRescaleFilter(1 / 10.0))]
          )
      )
  
      # reward is clipped
      reward_space = RewardSpace(1, -100, 100)
      filtered_reward_space = rescale_filter.get_filtered_reward_space(
          reward_space
      )
  
      # make sure the new reward space shape is calculated correctly
      assert filtered_reward_space.shape == 1
      assert filtered_reward_space.low == -10
      assert filtered_reward_space.high == 10
  
      # unbounded rewards
      reward_space = RewardSpace(1, -np.inf, np.inf)
      filtered_reward_space = rescale_filter.get_filtered_reward_space(
          reward_space
      )
  
      # make sure the new reward space shape is calculated correctly
      assert filtered_reward_space.shape == 1
      assert filtered_reward_space.low == -np.inf
      assert filtered_reward_space.high == np.inf
  
file_path: coach/rl_coach/tests/filters/reward/test_reward_rescale_filter.py
imports: import os
  import sys
  import pytest
  import numpy
  from rl_coach.filters.reward.reward_rescale_filter import RewardRescaleFilter
  from rl_coach.spaces import RewardSpace
  from rl_coach.core_types import EnvResponse
  from rl_coach.filters.filter import InputFilter
  from collections import OrderedDict
label_desc: Use image node_id 2 for calling a global function with example usage: test_get_filtered_reward_space() without return types