class_docstr: HalfCheetah Environment with two diff tasks, moving forwards or backwards
  
  Direction is defined as a scalar: +1.0 (forwards) or -1.0 (backwards)
class_docstr_tok: ["HalfCheetah","Environment","with","two","diff","tasks",",","moving","forwards","or","backwards","Direction","is","defined","as","a","scalar",":","+1.0","(","forwards",")","or","-1.0","(","backwards",")"]
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_direction
  
file_path: ray/python/ray/rllib/examples/env/halfcheetah_rand_direc.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 HalfCheetahRandDirecEnv obj's underlying member method code with example usage: obj.get_task() and returns: self