code: def __getitem__(self, n: int) -> Tuple[Tensor, Tensor, int, str]:
      noisy_path_A = Path(self._walker[n])
      noisy_path_B = str(noisy_path_A).replace("_A.wav", "_B.wav")
      clean_path = (
          noisy_path_A.parent.parent
          / "labels"
          / noisy_path_A.name.replace("_A.wav", ".wav")
      )
      transcript_path = str(clean_path).replace("wav", "txt")
      waveform_noisy_A, sample_rate1 = torchaudio.load(noisy_path_A)
      waveform_noisy_B, sample_rate2 = torchaudio.load(noisy_path_B)
      waveform_noisy = torch.cat(
          (waveform_noisy_A, waveform_noisy_B), dim=0
      )
      waveform_clean, sample_rate3 = torchaudio.load(clean_path)
      assert (
          sample_rate1 == _SAMPLE_RATE
          and sample_rate2 == _SAMPLE_RATE
          and sample_rate3 == _SAMPLE_RATE
      )
      with open(transcript_path, "r") as f:
          transcript = f.readline()
      return waveform_noisy, waveform_clean, _SAMPLE_RATE, transcript
  
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 3 for calling the L3DAS22 obj's underlying member method code with example usage: obj.__getitem__(n) and returns: waveform_noisy,  waveform_clean,  _SAMPLE_RATE,  transcript