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 reset_model(self):
      qpos = self.init_qpos + self.np_random.uniform(
          size=self.model.nq, low=-0.1, high=0.1
      )
      qvel = self.init_qvel + self.np_random.randn(self.model.nv) * 0.1
      self.set_state(qpos, qvel)
      return self._get_obs()
  
file_path: ray/python/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 7 for calling the AntRandGoalEnv obj's underlying member method code with example usage: obj.reset_model() and returns: self