class_docstr: Simulates a garage policy object.
  
  Args:
      scripted_actions (list or dictionary): data structure indexed by
          observation, returns a corresponding action
      agent_env_infos (list or dictionary): data structure indexed by
          observation, returns a corresponding agent_env_info
docstr: Return multiple actions.
  
  Args:
      observations (numpy.ndarray): Observations.
  
  Returns:
      list[int]: Actions given input observations.
      dict[dict]: Agent info indexed by observation.
docstr_tok: ["Return","multiple","actions",".","Args",":","observations","(","numpy.ndarray",")",":","Observations",".","Returns",":","list","[","int","]",":","Actions","given","input","observations",".","dict","[","dict","]",":","Agent","info","indexed","by","observation","."]
code: def get_actions(self, observations):
      """Return multiple actions.
  
      Args:
          observations (numpy.ndarray): Observations.
  
      Returns:
          list[int]: Actions given input observations.
          dict[dict]: Agent info indexed by observation.
  
      """
      if self._agent_env_infos:
          a_info = self._agent_env_infos[observations[0]]
      else:
          a_info = dict()
      return [
          self._scripted_actions[obs] for obs in observations
      ], a_info
  
imports: from garage.np.policies.policy import Policy
label_desc: Use image node_id 5 for calling the ScriptedPolicy obj's underlying member method code with example usage: obj.get_actions(observations) and returns: unknown,  a_info