docstr: Transparently configure optimizers for the inner model,
  unless one-shot algorithm has its own optimizer (via :meth:`configure_architecture_optimizers`),
  in which case, the optimizer will be appended to the list.
  
  The return value is still one of the 6 types defined in PyTorch-Lightning.
code: def configure_optimizers(self) -> Any:
      """
      Transparently configure optimizers for the inner model,
      unless one-shot algorithm has its own optimizer (via :meth:`configure_architecture_optimizers`),
      in which case, the optimizer will be appended to the list.
  
      The return value is still one of the 6 types defined in PyTorch-Lightning.
      """
      arch_optimizers = self.configure_architecture_optimizers() or []
      if not arch_optimizers:  # no architecture optimizer available
          return self.training_module.configure_optimizers()
  
      if isinstance(arch_optimizers, optim.Optimizer):
          arch_optimizers = [arch_optimizers]
  
      # Set the flag to True so that they can differ from other optimizers
      for optimizer in arch_optimizers:
          optimizer.is_arch_optimizer = True  # type: ignore
  
      optim_conf: Any = self.training_module.configure_optimizers()
  
      optim_conf = self.postprocess_weight_optimizers(optim_conf)
  
      # 0. optimizer is none
      if optim_conf is None:
          return arch_optimizers
      # 1. single optimizer
      if isinstance(optim_conf, Optimizer):
          return [optim_conf] + arch_optimizers
      # 2. two lists, optimizer + lr schedulers
      if (
          isinstance(optim_conf, (list, tuple))
          and len(optim_conf) == 2
          and isinstance(optim_conf[0], list)
          and all(isinstance(opt, Optimizer) for opt in optim_conf[0])
      ):
          return list(optim_conf[0]) + arch_optimizers, optim_conf[1]
      # 3. single dictionary
      if isinstance(optim_conf, dict):
          return [optim_conf] + [
              {"optimizer": optimizer} for optimizer in arch_optimizers
          ]
      # 4. multiple dictionaries
      if isinstance(optim_conf, (list, tuple)) and all(
          isinstance(d, dict) for d in optim_conf
      ):
          return list(optim_conf) + [
              {"optimizer": optimizer} for optimizer in arch_optimizers
          ]
      # 5. single list or tuple, multiple optimizer
      if isinstance(optim_conf, (list, tuple)) and all(
          isinstance(opt, Optimizer) for opt in optim_conf
      ):
          return list(optim_conf) + arch_optimizers
      # unknown configuration
      warnings.warn(
          "Unknown optimizer configuration. Architecture optimizers will be ignored. Strategy might fail.",
          UserWarning,
      )
  
      return optim_conf
  
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 11 for calling the BaseOneShotLightningModule obj's underlying member method code with example usage: obj.configure_optimizers() and returns: optim_conf, self, arch_optimizers, unknown, unknown,  optim_conf, unknown, unknown, unknown