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: Samples task of the meta-environment
  
  Args:
      n_tasks (int) : number of different meta-tasks needed
  
  Returns:
      tasks (list) : an (n_tasks) length list of tasks
docstr_tok: ["Samples","task","of","the","meta-environment","Args",":","n_tasks","(","int",")",":","number","of","different","meta-tasks","needed","Returns",":","tasks","(","list",")",":","an","(","n_tasks",")","length","list","of","tasks"]
code: def sample_tasks(self, n_tasks: int) -> List[TaskType]:
      """Samples task of the meta-environment
  
      Args:
          n_tasks (int) : number of different meta-tasks needed
  
      Returns:
          tasks (list) : an (n_tasks) length list of tasks
      """
      raise NotImplementedError
  
imports: import gymnasium
  from typing import List, Any
label_desc: Use image node_id 1 for calling the TaskSettableEnv obj's underlying member method code with example usage: obj.sample_tasks(n_tasks) without return types