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: Create a LightningMoudle that integrates pytorch modules, loss, optimizer.
  
  :param model:       Pytorch model to be converted.
  :param loss:        A torch loss function.
  :param optimizer:   A torch optimizer.
  :param scheduler:   A torch scheduler.
  :param metrics:     A list of metrics to calculate accuracy of the model.
docstr_tok: ["Create","a","LightningMoudle","that","integrates","pytorch","modules",",","loss",",","optimizer",".",":","param","model",":","Pytorch","model","to","be","converted",".",":","param","loss",":","A","torch","loss","function",".",":","param","optimizer",":","A","torch","optimizer",".",":","param","scheduler",":","A","torch","scheduler",".",":","param","metrics",":","A","list","of","metrics","to","calculate","accuracy","of","the","model","."]
code: def __init__(
      self,
      model: nn.Module,
      loss: _Loss = None,
      optimizer: Optimizer = None,
      scheduler: _LRScheduler = None,
      metrics: List[Metric] = None,
  ):
      """
      Create a LightningMoudle that integrates pytorch modules, loss, optimizer.
  
      :param model:       Pytorch model to be converted.
      :param loss:        A torch loss function.
      :param optimizer:   A torch optimizer.
      :param scheduler:   A torch scheduler.
      :param metrics:     A list of metrics to calculate accuracy of the model.
      """
      super().__init__()
      self.model = model
      self.loss = loss
      self.optimizer = optimizer
      self.scheduler = scheduler
      self.metrics = metrics
      self._forward_args_ = None
  
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 1 to create a new LightningModule object from inherited base classes: pl with example: obj = LightningModule(model, loss, optimizer, scheduler, metrics)