class_docstr: Interface definition for reinforcement learning environments
  defining the minimum required functionality for being considered an environment.
docstr: Environment step function.
  
  :param action: the selected action to take.
  :return: state, reward, done, info
docstr_tok: ["Environment","step","function",".",":","param","action",":","the","selected","action","to","take",".",":","return",":","state",",","reward",",","done",",","info"]
code: def step(self, action: Any) -> Tuple[Any, Any, bool, Dict[Any, Any]]:
      """Environment step function.
  
      :param action: the selected action to take.
      :return: state, reward, done, info
      """
  
file_path: maze/maze/core/env/base_env.py
imports: from abc import ABC, abstractmethod
  from typing import Tuple, Any, Dict
label_desc: Use image node_id 1 for calling the BaseEnv obj's underlying member method code with example usage: obj.step(action) without return types