class_docstr: Extension of gym.Env to define a task-settable Env.
  
  Your env must implement this interface in order to be used with MAML.
  For curriculum learning, you can add this API to your env such that
  the `env_task_fn` can set the next task as needed.
  
  Supports:
  - Sampling from a distribution of tasks for meta-learning.
  - Setting the env to any task it supports.
  - Getting the current task this env has been set to.
  
  .. testcode::
      :skipif: True
  
      from ray.rllib.env.apis.task_settable_env import TaskSettableEnv
      env = TaskSettableEnv(...)
docstr: Sets the specified task to the current environment
  
  Args:
      task: task of the meta-learning environment
docstr_tok: ["Sets","the","specified","task","to","the","current","environment","Args",":","task",":","task","of","the","meta-learning","environment"]
code: def set_task(self, task: TaskType) -> None:
      """Sets the specified task to the current environment
  
      Args:
          task: task of the meta-learning environment
      """
      raise NotImplementedError
  
imports: import gymnasium
  from typing import List, Any
label_desc: Use image node_id 2 for calling the TaskSettableEnv obj's underlying member method code with example usage: obj.set_task(task) without return types