code: import numpy as np
  from gym.spaces import Box
  
  from metaworld.envs.env_util import get_asset_full_path
  from metaworld.envs.mujoco.sawyer_xyz.sawyer_xyz_env import SawyerXYZEnv, _assert_task_is_set
  
  
  class SawyerLeverPullEnv(SawyerXYZEnv):
  
      def __init__(self):
  
          hand_low = (-0.5, 0.40, -0.15)
          hand_high = (0.5, 1, 0.5)
          obj_low = (-0.1, 0.7, 0.05)
          obj_high = (0.1, 0.8, 0.05)
  
          super().__init__(
              self.model_name,
              hand_low=hand_low,
              hand_high=hand_high,
          )
  
          self.init_config = {
              'obj_init_pos': np.array([0, 0.7, 0.05]),
              'hand_init_pos': np.array([0, 0.6, 0.2], dtype=np.float32),
          }
          self.goal = np.array([0, 0.75, -0.12])
          self.obj_init_pos = self.init_config['obj_init_pos']
          self.hand_init_pos = self.init_config['hand_init_pos']
  
          goal_low = self.hand_low
          goal_high = self.hand_high
  
          
  
          self._random_reset_space = Box(
              np.array(obj_low),
              np.array(obj_high),
          )
          self.goal_space = Box(np.array(goal_low), np.array(goal_high))
  
      @property
      def model_name(self):
          return get_asset_full_path('sawyer_xyz/sawyer_lever_pull.xml')
  
      @_assert_task_is_set
      def step(self, action):
          ob = super().step(action)
          reward, reachDist, pullDist = self.compute_reward(action, ob)
          self.curr_path_length += 1
  
          info = {
              'reachDist': reachDist,
              'goalDist': pullDist,
              'epRew': reward,
              'pickRew': None,
              'success': float(pullDist <= 0.05)
          }
  
          return ob, reward, False, info
  
      def _get_pos_objects(self):
          return self._get_site_pos('leverStart')
  
      def reset_model(self):
          self._reset_hand()
          self._target_pos = self.goal.copy()
          self.obj_init_pos = self.init_config['obj_init_pos']
  
          if self.random_init:
              goal_pos = self._get_state_rand_vec()
              self.obj_init_pos = goal_pos[:3]
              final_pos = goal_pos.copy()
              final_pos[1] += 0.05
              final_pos[2] -= 0.17
              self._target_pos = final_pos
  
          self.sim.model.body_pos[self.model.body_name2id('lever')] = self.obj_init_pos
          self.maxPullDist = np.linalg.norm(self._target_pos - self.obj_init_pos)
  
          return self._get_obs()
  
      def _reset_hand(self):
          super()._reset_hand(10)
  
          rightFinger, leftFinger = self._get_site_pos('rightEndEffector'), self._get_site_pos('leftEndEffector')
          self.init_fingerCOM  =  (rightFinger + leftFinger)/2
          self.reachCompleted = False
  
      def compute_reward(self, actions, obs):
          del actions
  
          objPos = obs[3:6]
  
          rightFinger, leftFinger = self._get_site_pos('rightEndEffector'), self._get_site_pos('leftEndEffector')
          fingerCOM  =  (rightFinger + leftFinger)/2
  
          pullGoal = self._target_pos
  
          pullDist = np.linalg.norm(objPos - pullGoal)
          reachDist = np.linalg.norm(objPos - fingerCOM)
          reachRew = -reachDist
  
          self.reachCompleted = reachDist < 0.05
  
          def pullReward():
              c1 = 1000
              c2 = 0.01
              c3 = 0.001
  
              if self.reachCompleted:
                  pullRew = 1000*(self.maxPullDist - pullDist) + c1*(np.exp(-(pullDist**2)/c2) + np.exp(-(pullDist**2)/c3))
                  pullRew = max(pullRew,0)
                  return pullRew
              else:
                  return 0
  
          pullRew = pullReward()
          reward = reachRew + pullRew
  
          return [reward, reachDist, pullDist]
  
apis: numpy.array numpy.linalg.norm numpy.exp