class_docstr: Same wrapper as in OpenAI baselines for comparability of results.
docstr: Return only every `skip`-th frame.
docstr_tok: ["Return","only","every","`","skip","`","-th","frame","."]
code: def __init__(self, env, skip=4):
      """Return only every `skip`-th frame."""
      gym.Wrapper.__init__(self, env)
      observation_space = env.observation_space
      # Most recent raw observations (for max pooling across time steps).
      self._obs_buffer = np.zeros(
          (2,) + observation_space.shape, dtype=observation_space.dtype
      )
      self._skip = skip
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  from absl import logging
  import gym
  import gym.wrappers
  import numpy
  from PIL import Image
label_desc: Use image node_id 1 to create a new MaxAndSkipEnv object from inherited base classes: gym with example: obj = MaxAndSkipEnv(env, skip)