class_docstr: A dummy akro.Dict environment with predefined inner spaces.
  
  Args:
      random (bool): If observations are randomly generated or not.
      obs_space_type (str): The type of the inner spaces of the
          dict observation space.
      act_space_type (str): The type of action space to mock.
class_docstr_tok: ["A","dummy","akro.Dict","environment","with","predefined","inner","spaces",".","Args",":","random","(","bool",")",":","If","observations","are","randomly","generated","or","not",".","obs_space_type","(","str",")",":","The","type","of","the","inner","spaces","of","the","dict","observation","space",".","act_space_type","(","str",")",":","The","type","of","action","space","to","mock","."]
docstr: Return the observation space.
  
  Returns:
      akro.Dict: Observation space.
docstr_tok: ["Return","the","observation","space",".","Returns",":","akro.Dict",":","Observation","space","."]
code: def observation_space(self):
      """Return the observation space.
  
      Returns:
          akro.Dict: Observation space.
      """
      if self.obs_space_type == "box":
          return gym.spaces.Dict(
              {
                  "achieved_goal": gym.spaces.Box(
                      low=-200.0,
                      high=200.0,
                      shape=(3,),
                      dtype=np.float32,
                  ),
                  "desired_goal": gym.spaces.Box(
                      low=-200.0,
                      high=200.0,
                      shape=(3,),
                      dtype=np.float32,
                  ),
                  "observation": gym.spaces.Box(
                      low=-200.0,
                      high=200.0,
                      shape=(25,),
                      dtype=np.float32,
                  ),
              }
          )
      elif self.obs_space_type == "image":
          return gym.spaces.Dict(
              {
                  "dummy": gym.spaces.Box(
                      low=0,
                      high=255,
                      shape=(100, 100, 3),
                      dtype=np.uint8,
                  ),
              }
          )
      else:
          return gym.spaces.Dict({"dummy": gym.spaces.Discrete(5)})
  
file_path: garage/tests/fixtures/envs/dummy/dummy_dict_env.py
imports: import akro
  import gym
  import numpy
  from garage import EnvSpec
  from tests.fixtures.envs.dummy import DummyEnv
label_desc: Use image node_id 2 for calling the DummyDictEnv obj's underlying member method code with example usage: obj.observation_space() and returns: gym, gym, gym