text: import gym
  from numpy import vectorize
  from tasks.base.vec_task import VecTask
  from gym import spaces
  
  from isaacgym import gymtorch
  from isaacgym.torch_utils import to_torch
  import torch
  import numpy as np
  
  class CustomEnv(gym.Env, VecTask):
  
      def __init__(self):
          super(CustomEnv, self).__init__()
          # Define action and observation space
          # They must be gym.spaces objects
          # Example when using discrete actions:
          self.action_space = self.act_space
          # Example for using image as input (channel-first; channel-last also works):
          self.observation_space = self.obs_space
  
      def step(self, actions):
          actions_tensor = torch.clamp(actions, -self.clip_actions, self.clip_actions)
  
          self.task.step(actions_tensor)
  
          return torch.clamp(self.task.obs_buf, -self.clip_obs, self.clip_obs).to(self.rl_device), self.task.rew_buf.to(self.rl_device), self.task.reset_buf.to(self.rl_device), self.task.extras
      def reset(self):
          actions = 0.01 * (1 - 2 * torch.rand([self.task.num_envs, self.task.num_actions], dtype=torch.float32, device=self.rl_device))
  
          # step the simulator
          self.task.step(actions)
          return torch.clamp(self.task.obs_buf, -self.clip_obs, self.clip_obs).to(self.rl_device)
      def render(self, mode='human'):
          return
      def close (self):
          return