class_docstr: Environment wrapper for RL2.
  
  In RL2, observation is concatenated with previous action,
  reward and terminal signal to form new observation.
  
  Args:
      env (Environment): An env that will be wrapped.
class_docstr_tok: ["Environment","wrapper","for","RL2",".","In","RL2",",","observation","is","concatenated","with","previous","action",",","reward","and","terminal","signal","to","form","new","observation",".","Args",":","env","(","Environment",")",":","An","env","that","will","be","wrapped","."]
docstr: Call reset on wrapped env.
  
  Returns:
      numpy.ndarray: The first observation conforming to
          `observation_space`.
      dict: The episode-level information.
          Note that this is not part of `env_info` provided in `step()`.
          It contains information of he entire episode， which could be
          needed to determine the first action (e.g. in the case of
          goal-conditioned or MTRL.)
docstr_tok: ["Call","reset","on","wrapped","env",".","Returns",":","numpy.ndarray",":","The","first","observation","conforming","to","`","observation_space","`",".","dict",":","The","episode-level","information",".","Note","that","this","is","not","part","of","`","env_info","`","provided","in","`","step","(",")","`",".","It","contains","information","of","he","entire","episode\uff0c","which","could","be","needed","to","determine","the","first","action","(","e.g",".","in","the","case","of","goal-conditioned","or","MTRL",".",")"]
code: def reset(self):
      """Call reset on wrapped env.
  
      Returns:
          numpy.ndarray: The first observation conforming to
              `observation_space`.
          dict: The episode-level information.
              Note that this is not part of `env_info` provided in `step()`.
              It contains information of he entire episode， which could be
              needed to determine the first action (e.g. in the case of
              goal-conditioned or MTRL.)
  
      """
      first_obs, episode_info = self._env.reset()
      first_obs = np.concatenate(
          [first_obs, np.zeros(self._env.action_space.shape), [0], [0]]
      )
  
      return first_obs, episode_info
  
imports: import abc
  import collections
  import akro
  from dowel import logger
  import numpy
  from garage import EnvSpec, EnvStep, EpisodeBatch, log_multitask_performance, StepType, Wrapper
  from garage.np.algos import MetaRLAlgorithm
  from garage.sampler import DefaultWorker
  from garage.tf.algos._rl2npo import RL2NPO
label_desc: Use image node_id 4 for calling the RL2Env obj's underlying member method code with example usage: obj.reset() and returns: first_obs,  episode_info