code: def train_dataloader(self):
      datasets = [
          self.librispeech_cls(
              self.librispeech_path, url="train-clean-360"
          ),
          self.librispeech_cls(
              self.librispeech_path, url="train-clean-100"
          ),
          self.librispeech_cls(
              self.librispeech_path, url="train-other-500"
          ),
      ]
  
      if not self.train_dataset_lengths:
          self.train_dataset_lengths = [
              get_sample_lengths(dataset) for dataset in datasets
          ]
  
      dataset = torch.utils.data.ConcatDataset(
          [
              CustomBucketDataset(
                  dataset,
                  lengths,
                  self.max_tokens,
                  self.train_num_buckets,
                  batch_size=self.batch_size,
              )
              for dataset, lengths in zip(
                  datasets, self.train_dataset_lengths
              )
          ]
      )
      dataset = TransformDataset(dataset, self.train_transform)
      dataloader = torch.utils.data.DataLoader(
          dataset,
          num_workers=self.num_workers,
          batch_size=None,
          shuffle=self.train_shuffle,
      )
      return dataloader
  
imports: import os
  import random
  import torch
  import torchaudio
  from pytorch_lightning import LightningDataModule
label_desc: Use image node_id 2 for calling the LibriSpeechDataModule obj's underlying member method code with example usage: obj.train_dataloader() and returns: dataloader