code: def train_dataloader(self):
      dataset = torch.utils.data.ConcatDataset(
          [
              L3DAS22(self.dataset_path, "train360"),
              L3DAS22(self.dataset_path, "train100"),
          ]
      )
      return torch.utils.data.DataLoader(
          dataset,
          batch_size=self.batch_size,
          collate_fn=CollateFnL3DAS22(
              audio_length=64000, rand_crop=True
          ),
          shuffle=True,
          num_workers=20,
      )
  
file_path: audio/examples/dnn_beamformer/datamodule.py
imports: from pathlib import Path
  from typing import Tuple, Union
  import lightning.pytorch
  import torch
  import torchaudio
  from torch import Tensor
  from torch.utils.data import Dataset
  from utils import CollateFnL3DAS22
label_desc: Use image node_id 2 for calling the L3DAS22DataModule obj's underlying member method code with example usage: obj.train_dataloader() and returns: torch