class_docstr: A generic TS Transformer LightningMoudle for light-weight HPO.
code: def training_step(self, batch, batch_idx):
      y_hat = self(*batch)
      target = batch[1][:, self.hparams.label_len :, :]
      loss = self.loss(y_hat, target)  # use last output as target
      self.log(
          "train/loss", loss, on_epoch=True, prog_bar=True, logger=True
      )
      return loss
  
imports: from bigdl.nano.pytorch.lightning import LightningModule
  from bigdl.nano.utils.common import invalidInputError
  from typing import List
  import pytorch_lightning
  from torchmetrics.metric import Metric
  from torch.optim.lr_scheduler import _LRScheduler
  import bigdl.nano.automl.hpo
  import warnings
  import torch
  import inspect
  from torch import nn, Tensor, fx
  from torch.utils.data import TensorDataset, DataLoader
  from bigdl.nano.automl.hpo.space import Space
label_desc: Use image node_id 4 for calling the GenericTSTransformerLightningModule obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: loss