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: Define a single predict step.
docstr_tok: ["Define","a","single","predict","step","."]
code: def predict_step(self, batch, batch_idx, dataloader_idx=0):
      """Define a single predict step."""
      return self(*batch[: self._nargs])
  
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 11 for calling the LightningModule obj's underlying member method code with example usage: obj.predict_step(batch, batch_idx, dataloader_idx) and returns: self