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)
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_direction = task
  
file_path: 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 3 for calling the HalfCheetahRandDirecEnv obj's underlying member method code with example usage: obj.set_task(task) without return types