class_docstr: Ant Environment that randomizes goals as tasks
  
  Goals are randomly sampled 2D positions
code: def viewer_setup(self):
      self.viewer.cam.distance = self.model.stat.extent * 0.5
  
file_path: ray/rllib_contrib/maml/src/rllib_maml/envs/ant_rand_goal.py
imports: import numpy
  from gymnasium.envs.mujoco.mujoco_env import MujocoEnv
  from gymnasium.utils import EzPickle
  from ray.rllib.env.apis.task_settable_env import TaskSettableEnv
label_desc: Use image node_id 8 for calling the AntRandGoalEnv obj's underlying member method code with example usage: obj.viewer_setup() without return types