text: import numpy as np
  import gym
  
  class Wrapper(gym.Wrapper):
      def __getattr__(self, name):
          return getattr(self.env, name)
  
  class TransformObservation(gym.wrappers.TransformObservation):
      def __getattr__(self, name):
          return getattr(self.env, name)
  
  class IntersimpleTimeLimit(gym.wrappers.TimeLimit):
      def __getattr__(self, name):
          return getattr(self.env, name)
  
  class CollisionPenaltyWrapper(Wrapper):
  
      def __init__(self, env, collision_distance, collision_penalty, *args, **kwargs):
          super().__init__(env, *args, **kwargs)
          self.penalty = collision_penalty
          self.distance = collision_distance
      
      def step(self, action):
          obs, reward, done, info = super().step(action)
          reward = -self.penalty if (obs.reshape(-1, 6)[1:, 0] < self.distance).any() else reward
  
          self.env._rewards.pop()
          self.env._rewards.append(reward)
  
          return obs, reward, done, info
  
  class Minobs(Wrapper):
      """ Meant to be used as wrapper around LidarObservation """
  
      def __init__(self, env, *args, **kwargs):
          super().__init__(env, *args, **kwargs)
          n_rays = int(self.observation_space.shape[0] / 6) - 1
          self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=((1 + n_rays) * 2,))
      
      def minobs(self, obs):
          """ ego v, psidot ; (for each ray,) rel. distance, rel. velocity in ego forward direction """
          obs = obs.reshape(-1, 6)
          obs = np.concatenate((obs[:1, [2, 4]], obs[1:, [0, 2]]), axis=0)
          return obs.reshape(-1)
  
      def reset(self):
          return self.minobs(super().reset())
      
      def step(self, action):
          obs, reward, done, info = super().step(action)
          return self.minobs(obs), reward, done, info
  
  class Setobs(Wrapper):
      """ Meant to be used as wrapper around LidarObservation """
  
      def __init__(self, env, *args, **kwargs):
          super().__init__(env, *args, **kwargs)
          self.n_rays = int(self.observation_space.shape[0] / 6) - 1
          self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(self.n_rays, 6))
      
      def obs(self, obs):
          obs = obs.reshape(-1, 6)
  
          ego = obs[:1, [2, 4]] # v, psidot
          ego = np.tile(ego, (self.n_rays, 1))
          
          other = obs[1:, [0, 1, 2]] # distance, angle, velocity component in ego forward direction
          other = np.stack((other[:, 0], np.cos(other[:, 1]), np.sin(other[:, 1]), other[:, 2]), axis=-1)
          
          obs = np.concatenate((ego, other), axis=-1) 
          return obs
  
      def reset(self):
          return self.obs(super().reset())
      
      def step(self, action):
          obs, reward, done, info = super().step(action)
          return self.obs(obs), reward, done, info
  