class_docstr: Note that for now, we will still follow the Dataset format stated in
  https://github.com/fjxmlzn/DoppelGANger#dataset-format.
  
  Please notice that this module can not work alone without doppelganger_torch.
class_docstr_tok: ["Note","that","for","now",",","we","will","still","follow","the","Dataset","format","stated","in","https",":","\/\/github.com\/fjxmlzn\/DoppelGANger","#","dataset-format",".","Please","notice","that","this","module","can","not","work","alone","without","doppelganger_torch","."]
code: def train_dataloader(self):
      self.data_feature = torch.from_numpy(self.data_feature).float()
      self.data_attribute = torch.from_numpy(
          self.data_attribute
      ).float()
      dataset = CustomizedDataset(
          self.data_feature, self.data_attribute
      )
      return DataLoader(
          dataset, batch_size=self.batch_size, shuffle=True
      )
  
file_path: BigDL/python/chronos/src/bigdl/chronos/simulator/doppelganger/data_module.py
imports: import os
  import pickle
  import numpy
  import sys
  import torch
  from torch.utils.data import DataLoader, Dataset
  from pytorch_lightning import LightningDataModule
  from .util import add_gen_flag, normalize_per_sample, renormalize_per_sample
label_desc: Use image node_id 2 for calling the DoppelGANgerDataModule obj's underlying member method code with example usage: obj.train_dataloader() and returns: DataLoader