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 LightningModule.load_state_dict, execept falling back to pytorch.
docstr_tok: ["Same","as","LightningModule.load_state_dict",",","execept","falling","back","to","pytorch","."]
code: def load_state_dict(
      self,
      state_dict: "OrderedDict[str, Tensor]",  # type: ignore
      strict: bool = True,
  ):
      """Same as LightningModule.load_state_dict, execept falling back to pytorch."""
      try:
          super().load_state_dict(state_dict)
      except RuntimeError:
          self.model.load_state_dict(state_dict)
  
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 13 for calling the LightningModule obj's underlying member method code with example usage: obj.load_state_dict(state_dict, strict) without return types