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 a single action.
  
  Args:
      observation (numpy.ndarray): Observations.
  
  Returns:
      int: Action given input observation.
      dict[dict]: Agent infos indexed by observation.
docstr_tok: ["Return","a","single","action",".","Args",":","observation","(","numpy.ndarray",")",":","Observations",".","Returns",":","int",":","Action","given","input","observation",".","dict","[","dict","]",":","Agent","infos","indexed","by","observation","."]
code: def get_action(self, observation):
      """Return a single action.
  
      Args:
          observation (numpy.ndarray): Observations.
  
      Returns:
          int: Action given input observation.
          dict[dict]: Agent infos indexed by observation.
  
      """
      if self._agent_env_infos:
          a_info = self._agent_env_infos[observation]
      else:
          a_info = dict()
      return self._scripted_actions[observation], a_info
  
imports: from garage.np.policies.policy import Policy
label_desc: Use image node_id 4 for calling the ScriptedPolicy obj's underlying member method code with example usage: obj.get_action(observation) and returns: self,  a_info