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: Returns:
      task: task of the meta-learning environment
docstr_tok: ["Returns",":","task",":","task","of","the","meta-learning","environment"]
code: def get_task(self):
      """
      Returns:
          task: task of the meta-learning environment
      """
      return self.goal_pos
  
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 4 for calling the AntRandGoalEnv obj's underlying member method code with example usage: obj.get_task() and returns: self