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: Reset the environment.
  
  Returns:
      numpy.ndarray: Observation after reset.
docstr_tok: ["Reset","the","environment",".","Returns",":","numpy.ndarray",":","Observation","after","reset","."]
code: def reset(self):
      """Reset the environment.
  
      Returns:
          numpy.ndarray: Observation after reset.
  
      """
      return self.observation_space.sample()
  
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 4 for calling the DummyDictEnv obj's underlying member method code with example usage: obj.reset() and returns: self