docstr: Get the optimizers configured in :meth:`configure_architecture_optimizers`.
  
  Return type would be LightningOptimizer or list of LightningOptimizer.
code: def architecture_optimizers(
      self,
  ) -> list[LightningOptimizer] | LightningOptimizer | None:
      """
      Get the optimizers configured in :meth:`configure_architecture_optimizers`.
  
      Return type would be LightningOptimizer or list of LightningOptimizer.
      """
      optimizers = self.optimizers()
      if not isinstance(optimizers, list):
          optimizers = [optimizers]
      optimizers = [
          opt
          for opt in optimizers
          if getattr(opt, "is_arch_optimizer", False)
      ]
      if not optimizers:
          return None
      if len(optimizers) == 1:
          return optimizers[0]
      return optimizers
  
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 20 for calling the BaseOneShotLightningModule obj's underlying member method code with example usage: obj.architecture_optimizers() and returns: optimizers, None, optimizers