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"]
docstr: Args:
      task: task of the meta-learning environment
docstr_tok: ["Args",":","task",":","task","of","the","meta-learning","environment"]
code: def set_task(self, task):
      """
      Args:
          task: task of the meta-learning environment
      """
      self.goal_pos = task
  
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 3 for calling the AntRandGoalEnv obj's underlying member method code with example usage: obj.set_task(task) without return types