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","."]
code: def __init__(
      self, random=True, obs_space_type="box", act_space_type="box"
  ):
      assert obs_space_type in ["box", "image", "discrete"]
      assert act_space_type in ["box", "discrete"]
      super().__init__(random)
      self.obs_space_type = obs_space_type
      self.act_space_type = act_space_type
      self.spec = EnvSpec(
          action_space=self.action_space,
          observation_space=self.observation_space,
      )
  
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 1 to create a new DummyDictEnv object from inherited base classes: DummyEnv with example: obj = DummyDictEnv(random, obs_space_type, act_space_type)