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.
code: def forward_args(self):  # noqa
      if self._forward_args_ is None:
          self._forward_args_ = inspect.getfullargspec(
              self.model.forward
          ).args[1:]
      return self._forward_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 2 for calling the LightningModule obj's underlying member method code with example usage: obj.forward_args() and returns: self