docstr: Advance the learning rates, when manual optimization is turned on.
  
  The full implementation is
  `here <https://github.com/Lightning-AI/lightning/blob/0e531283/src/pytorch_lightning/loops/epoch/training_epoch_loop.py>`__.
  We only include a partial implementation here.
  Advanced features like Reduce-lr-on-plateau are not supported.
code: def advance_lr_schedulers(self, batch_idx: int):
      """
      Advance the learning rates, when manual optimization is turned on.
  
      The full implementation is
      `here <https://github.com/Lightning-AI/lightning/blob/0e531283/src/pytorch_lightning/loops/epoch/training_epoch_loop.py>`__.
      We only include a partial implementation here.
      Advanced features like Reduce-lr-on-plateau are not supported.
      """
      if self.automatic_optimization:
          raise ValueError(
              "This method should not be used when automatic optimization is turned on."
          )
  
      self._advance_lr_schedulers_impl(batch_idx, "step")
      if self.trainer.is_last_batch:
          self._advance_lr_schedulers_impl(batch_idx, "epoch")
  
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 18 for calling the BaseOneShotLightningModule obj's underlying member method code with example usage: obj.advance_lr_schedulers(batch_idx) without return types