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","."]
code: def __init__(self, env):
      super().__init__(env)
  
      self._observation_space = self._create_rl2_obs_space()
      self._spec = EnvSpec(
          action_space=self.action_space,
          observation_space=self._observation_space,
          max_episode_length=self._env.spec.max_episode_length,
      )
  
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 1 to create a new RL2Env object from inherited base classes: Wrapper with example: obj = RL2Env(env)