class_docstr: A generic TS Transformer LightningMoudle for light-weight HPO.
docstr: Same as torch.nn.Module.forward().
docstr_tok: ["Same","as","torch.nn.Module.forward","(",")","."]
code: def forward(self, *args):
      """Same as torch.nn.Module.forward()."""
      nargs = len(inspect.getfullargspec(self.model.forward).args[1:])
      if isinstance(args, fx.Proxy):
          args = [args[i] for i in range(nargs)]
      else:
          args = args[:nargs]
      batch_x, batch_y, batch_x_mark, batch_y_mark = map(
          lambda x: x.float(), args
      )
      outputs = self.model(batch_x, batch_x_mark, batch_y, batch_y_mark)
      return outputs
  
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 3 for calling the GenericTSTransformerLightningModule obj's underlying member method code with example usage: obj.forward() and returns: outputs