code: def __init__(
      self,
      xml_file="walker2d.xml",
      forward_reward_weight=1.0,
      ctrl_cost_weight=1e-3,
      healthy_reward=1.0,
      terminate_when_unhealthy=True,
      healthy_z_range=(0.8, 2.0),
      healthy_angle_range=(-1.0, 1.0),
      reset_noise_scale=5e-3,
      exclude_current_positions_from_observation=True,
      **kwargs,
  ):
      utils.EzPickle.__init__(
          self,
          xml_file,
          forward_reward_weight,
          ctrl_cost_weight,
          healthy_reward,
          terminate_when_unhealthy,
          healthy_z_range,
          healthy_angle_range,
          reset_noise_scale,
          exclude_current_positions_from_observation,
          **kwargs,
      )
  
      self._forward_reward_weight = forward_reward_weight
      self._ctrl_cost_weight = ctrl_cost_weight
  
      self._healthy_reward = healthy_reward
      self._terminate_when_unhealthy = terminate_when_unhealthy
  
      self._healthy_z_range = healthy_z_range
      self._healthy_angle_range = healthy_angle_range
  
      self._reset_noise_scale = reset_noise_scale
  
      self._exclude_current_positions_from_observation = (
          exclude_current_positions_from_observation
      )
  
      if exclude_current_positions_from_observation:
          observation_space = Box(
              low=-np.inf, high=np.inf, shape=(17,), dtype=np.float64
          )
      else:
          observation_space = Box(
              low=-np.inf, high=np.inf, shape=(18,), dtype=np.float64
          )
  
      MuJocoPyEnv.__init__(
          self,
          xml_file,
          4,
          observation_space=observation_space,
          **kwargs,
      )
  
file_path: gym/gym/envs/mujoco/walker2d_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 1 to create a new Walker2dEnv object from inherited base classes: MuJocoPyEnv, utils with example: obj = Walker2dEnv(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy, healthy_z_range, healthy_angle_range, reset_noise_scale, exclude_current_positions_from_observation)