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: Called at the beginning of training after sanity check.
docstr_tok: ["Called","at","the","beginning","of","training","after","sanity","check","."]
code: def on_train_start(self) -> None:
      """Called at the beginning of training after sanity check."""
      invalidInputError(
          self.loss, "Loss must not be None for training."
      )
      return super().on_train_start()
  
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 7 for calling the LightningModule obj's underlying member method code with example usage: obj.on_train_start() and returns: super