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: Same as torch.nn.Module.forward().
docstr_tok: ["Same","as","torch.nn.Module.forward","(",")","."]
code: def forward(self, *args):
      """Same as torch.nn.Module.forward()."""
      if isinstance(args, fx.Proxy):
          args = [args[i] for i in range(self._nargs)]
      else:
          args = args[: self._nargs]
      return self.model(*args)
  
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 6 for calling the LightningModule obj's underlying member method code with example usage: obj.forward() and returns: self