class_docstr: Ant Environment that randomizes goals as tasks
  
  Goals are randomly sampled 2D positions
class_docstr_tok: ["Ant","Environment","that","randomizes","goals","as","tasks","Goals","are","randomly","sampled","2D","positions"]
code: def _get_obs(self):
      return np.concatenate(
          [
              self.sim.data.qpos.flat,
              self.sim.data.qvel.flat,
              np.clip(self.sim.data.cfrc_ext, -1, 1).flat,
          ]
      )
  
file_path: ray/rllib/examples/env/ant_rand_goal.py
imports: from gymnasium.envs.mujoco.mujoco_env import MujocoEnv
  from gymnasium.utils import EzPickle
  import numpy
  from ray.rllib.env.apis.task_settable_env import TaskSettableEnv
label_desc: Use image node_id 6 for calling the AntRandGoalEnv obj's underlying member method code with example usage: obj._get_obs() and returns: np