docstr: Hook kept for subclasses. A specific NAS method inheriting this base class should return its architecture optimizers here
  if architecture parameters are needed. Note that lr schedulers are not supported now for architecture_optimizers.
  
  Returns
  -------
  Optimizers used by a specific NAS algorithm. Return None if no architecture optimizers are needed.
code: def configure_architecture_optimizers(
      self,
  ) -> list[optim.Optimizer] | optim.Optimizer | None:
      """
      Hook kept for subclasses. A specific NAS method inheriting this base class should return its architecture optimizers here
      if architecture parameters are needed. Note that lr schedulers are not supported now for architecture_optimizers.
  
      Returns
      -------
      Optimizers used by a specific NAS algorithm. Return None if no architecture optimizers are needed.
      """
      return None
  
imports: from __future__ import annotations
  import warnings
  from typing import Any, Iterable, List, cast, TYPE_CHECKING
  import torch.optim
  import torch.nn
  from torch.optim import Optimizer
  import nni.nas.nn.pytorch
  from nni.nas.evaluator.pytorch import LightningModule, Trainer
  from nni.mutable import Sample
  from .supermodule.base import BaseSuperNetModule
label_desc: Use image node_id 15 for calling the BaseOneShotLightningModule obj's underlying member method code with example usage: obj.configure_architecture_optimizers() and returns: None