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 sample_tasks(self, n_tasks):
      # Samples a goal position (2x1 position ector)
      a = np.random.random(n_tasks) * 2 * np.pi
      r = 3 * np.random.random(n_tasks) ** 0.5
      return np.stack((r * np.cos(a), r * np.sin(a)), axis=-1)
  
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 2 for calling the AntRandGoalEnv obj's underlying member method code with example usage: obj.sample_tasks(n_tasks) and returns: np