class_docstr: Environment wrapper for Multigrid environments.
  
  Note: the main difference with vanilla GymWrapper is that reward_spec() is
  overridden and rewards are cast to np.arrays in step()
docstr: Returns the last info returned from env.step(action).
  
  Returns:
    info: dictionary of diagnostic information from the last environment step
docstr_tok: ["Returns","the","last","info","returned","from","env.step","(","action",")",".","Returns",":","info",":","dictionary","of","diagnostic","information","from","the","last","environment","step"]
code: def get_info(self) -> Optional[Dict[str, Any]]:
      """Returns the last info returned from env.step(action).
  
      Returns:
        info: dictionary of diagnostic information from the last environment step
      """
      return self._last_info
  
imports: from typing import Any, Dict, List, Optional
  import warnings
  from acme import specs
  from acme import types
  from acme import wrappers
  from acme.multiagent import types
  from acme.wrappers import multiagent_dict_key_wrapper
  import dm_env
  import gym
  from gym import spaces
  import jax
  import numpy
  import tree
label_desc: Use image node_id 8 for calling the MultigridWrapper obj's underlying member method code with example usage: obj.get_info() and returns: self