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: Step the environment.
  
  Args:
      action (int): Action input.
  
  Returns:
      np.ndarray: Observation.
      float: Reward.
      bool: If the environment is terminated.
      dict: Environment information.
docstr_tok: ["Step","the","environment",".","Args",":","action","(","int",")",":","Action","input",".","Returns",":","np.ndarray",":","Observation",".","float",":","Reward",".","bool",":","If","the","environment","is","terminated",".","dict",":","Environment","information","."]
code: def step(self, action):
      """Step the environment.
  
      Args:
          action (int): Action input.
  
      Returns:
          np.ndarray: Observation.
          float: Reward.
          bool: If the environment is terminated.
          dict: Environment information.
  
      """
      return self.observation_space.sample(), 0, True, dict()
  
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 5 for calling the DummyDictEnv obj's underlying member method code with example usage: obj.step(action) and returns: self,  int,  True,  dict