class_docstr: A wrapper LightningMoudle for common PyTorch models.
  
  This class implements common methods in LightningModule, so that classic pytorch
  supervised learning model only needs to supply module, loss and optimizer to create
  a LightningModule.
docstr: Setup the optimizers for this module, and return optimizers and schedulers.
docstr_tok: ["Setup","the","optimizers","for","this","module",",","and","return","optimizers","and","schedulers","."]
code: def configure_optimizers(self):
      """Setup the optimizers for this module, and return optimizers and schedulers."""
      optimizers = [self.optimizer]
      schedulers = []
      if self.scheduler:
          schedulers.append(self.scheduler)
      return optimizers, schedulers
  
imports: from collections import OrderedDict
  import inspect
  from typing import List
  from torchmetrics.metric import Metric
  import pytorch_lightning
  from torch import nn, Tensor, fx
  from torch.nn.modules.loss import _Loss
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
  from bigdl.nano.utils.common import invalidInputError
label_desc: Use image node_id 12 for calling the LightningModule obj's underlying member method code with example usage: obj.configure_optimizers() and returns: optimizers,  schedulers