class_docstr: A wrapper for an environment that implements the `Environment` API.
class_docstr_tok: ["A","wrapper","for","an","environment","that","implements","the","`","Environment","`","API","."]
docstr: Reset the 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: ["Reset","the","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):
      """Reset the 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.)
  
      """
      return self._env.reset()
  
imports: import abc
  from dataclasses import dataclass
  from typing import Dict
  import akro
  import numpy
  from garage._dtypes import StepType
label_desc: Use image node_id 8 for calling the Wrapper obj's underlying member method code with example usage: obj.reset() and returns: self