docstr: Some subclasss need to modify the original optimizers. This is where it should be done.
  For example, differentiable algorithms might not want the architecture weights to be inside the weight optimizers.
  
  Returns
  -------
  By default, it return the original object.
code: def postprocess_weight_optimizers(self, optimizers: Any) -> Any:
      """
      Some subclasss need to modify the original optimizers. This is where it should be done.
      For example, differentiable algorithms might not want the architecture weights to be inside the weight optimizers.
  
      Returns
      -------
      By default, it return the original object.
      """
      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 14 for calling the BaseOneShotLightningModule obj's underlying member method code with example usage: obj.postprocess_weight_optimizers(optimizers) and returns: optimizers