class_docstr: A generic TS Transformer LightningMoudle for light-weight HPO.
docstr: Create the train data loader.
docstr_tok: ["Create","the","train","data","loader","."]
code: def train_dataloader(self):
      """Create the train data loader."""
      return DataLoader(
          TensorDataset(
              torch.from_numpy(self.data[0]).float(),
              torch.from_numpy(self.data[1]).float(),
              torch.from_numpy(self.data[2]).float(),
              torch.from_numpy(self.data[3]).float(),
          ),
          batch_size=self.batch_size,
          shuffle=True,
      )
  
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 8 for calling the GenericTSTransformerLightningModule obj's underlying member method code with example usage: obj.train_dataloader() and returns: DataLoader