class_docstr: A DM Control Suite preprocessing wrapper.
  
  Attributes:
    environment: The underlying environment that is wrapped for preprocessing.
    game_over: True when the last environment step led to a game over signal.
class_docstr_tok: ["A","DM","Control","Suite","preprocessing","wrapper",".","Attributes",":","environment",":","The","underlying","environment","that","is","wrapped","for","preprocessing",".","game_over",":","True","when","the","last","environment","step","led","to","a","game","over","signal","."]
docstr: The observation space of the processed environment.
docstr_tok: ["The","observation","space","of","the","processed","environment","."]
code: def observation_space(self) -> spaces.Box:
      """The observation space of the processed environment."""
      obs_spec = self.environment.observation_spec()
      assert isinstance(obs_spec, collections.OrderedDict)
      # Since observations are flattened, all shapes are 1 dimensional
      low = list()
      high = list()
      for k in obs_spec:
          assert isinstance(obs_spec[k], specs.Array)
          if isinstance(obs_spec[k], specs.BoundedArray):
              low.extend(obs_spec[k].minimum.tolist())
              high.extend(obs_spec[k].maximum.tolist())
          else:
              low.extend(
                  [-float("inf") for _ in range(obs_spec[k].shape[0])]
              )
              high.extend(
                  [float("inf") for _ in range(obs_spec[k].shape[0])]
              )
      low = np.asarray(low, dtype=np.float32)
      high = np.asarray(high, dtype=np.float32)
      return spaces.Box(low=low, high=high, dtype=np.float32)
  
imports: import collections
  from typing import Any, Mapping, Tuple
  from dm_control import suite
  from dm_control.rl import control
  import dm_env
  from dm_env import specs
  import gin
  import gym
  from gym import spaces
  import numpy
label_desc: Use image node_id 2 for calling the DeepmindControlPreprocessing obj's underlying member method code with example usage: obj.observation_space() and returns: spaces