code: def step(self, action):
      xy_position_before = self.sim.data.qpos[0:2].copy()
      self.do_simulation(action, self.frame_skip)
      xy_position_after = self.sim.data.qpos[0:2].copy()
  
      xy_velocity = (xy_position_after - xy_position_before) / self.dt
      x_velocity, y_velocity = xy_velocity
  
      forward_reward = self._forward_reward_weight * x_velocity
      ctrl_cost = self.control_cost(action)
  
      observation = self._get_obs()
      reward = forward_reward - ctrl_cost
      info = {
          "reward_fwd": forward_reward,
          "reward_ctrl": -ctrl_cost,
          "x_position": xy_position_after[0],
          "y_position": xy_position_after[1],
          "distance_from_origin": np.linalg.norm(
              xy_position_after, ord=2
          ),
          "x_velocity": x_velocity,
          "y_velocity": y_velocity,
          "forward_reward": forward_reward,
      }
  
      if self.render_mode == "human":
          self.render()
  
      return observation, reward, False, False, info
  
file_path: gym/gym/envs/mujoco/swimmer_v3.py
imports: import numpy
  from gym import utils
  from gym.envs.mujoco import MuJocoPyEnv
  from gym.spaces import Box
label_desc: Use image node_id 3 for calling the SwimmerEnv obj's underlying member method code with example usage: obj.step(action) and returns: observation,  reward,  False,  False,  info