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: Function to compute new reward.
  
  Args:
      achieved_goal (numpy.ndarray): Achieved goal.
      goal (numpy.ndarray): Original desired goal.
      info (dict): Extra information.
  
  Returns:
      float: New computed reward.
docstr_tok: ["Function","to","compute","new","reward",".","Args",":","achieved_goal","(","numpy.ndarray",")",":","Achieved","goal",".","goal","(","numpy.ndarray",")",":","Original","desired","goal",".","info","(","dict",")",":","Extra","information",".","Returns",":","float",":","New","computed","reward","."]
code: def compute_reward(self, achieved_goal, goal, info):
      """Function to compute new reward.
  
      Args:
          achieved_goal (numpy.ndarray): Achieved goal.
          goal (numpy.ndarray): Original desired goal.
          info (dict): Extra information.
  
      Returns:
          float: New computed reward.
  
      """
      del info
      return np.sum(achieved_goal - goal)
  
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 6 for calling the DummyDictEnv obj's underlying member method code with example usage: obj.compute_reward(achieved_goal, goal, info) and returns: np