code: def __getitem__(self, index):
      filename = self.audio_files[index]
      if self._cache_ref_count == 0:
          audio, sampling_rate = load_wav(filename)
          audio = audio / MAX_WAV_VALUE
          if not self.fine_tuning:
              audio = normalize(audio) * 0.95
          self.cached_wav = audio
          if sampling_rate != self.sampling_rate:
              raise ValueError(
                  "{} SR doesn't match target {} SR".format(
                      sampling_rate, self.sampling_rate
                  )
              )
          self._cache_ref_count = self.n_cache_reuse
      else:
          audio = self.cached_wav
          self._cache_ref_count -= 1
  
      audio = torch.FloatTensor(audio)
      audio = audio.unsqueeze(0)
  
      if not self.fine_tuning:
          if self.split:
              if audio.size(1) >= self.segment_size:
                  max_audio_start = audio.size(1) - self.segment_size
                  audio_start = random.randint(0, max_audio_start)
                  audio = audio[
                      :, audio_start : audio_start + self.segment_size
                  ]
              else:
                  audio = torch.nn.functional.pad(
                      audio,
                      (0, self.segment_size - audio.size(1)),
                      "constant",
                  )
  
          mel = mel_spectrogram(
              audio,
              self.n_fft,
              self.num_mels,
              self.sampling_rate,
              self.hop_size,
              self.win_size,
              self.fmin,
              self.fmax,
              center=False,
          )
      else:
          mel = np.load(
              os.path.join(
                  self.base_mels_path,
                  os.path.splitext(os.path.split(filename)[-1])[0]
                  + ".npy",
              )
          )
          mel = torch.from_numpy(mel)
  
          if len(mel.shape) < 3:
              mel = mel.unsqueeze(0)
  
          if self.split:
              frames_per_seg = math.ceil(
                  self.segment_size / self.hop_size
              )
  
              if audio.size(1) >= self.segment_size:
                  mel_start = random.randint(
                      0, mel.size(2) - frames_per_seg - 1
                  )
                  mel = mel[
                      :, :, mel_start : mel_start + frames_per_seg
                  ]
                  audio = audio[
                      :,
                      mel_start
                      * self.hop_size : (mel_start + frames_per_seg)
                      * self.hop_size,
                  ]
              else:
                  mel = torch.nn.functional.pad(
                      mel, (0, frames_per_seg - mel.size(2)), "constant"
                  )
                  audio = torch.nn.functional.pad(
                      audio,
                      (0, self.segment_size - audio.size(1)),
                      "constant",
                  )
  
      mel_loss = mel_spectrogram(
          audio,
          self.n_fft,
          self.num_mels,
          self.sampling_rate,
          self.hop_size,
          self.win_size,
          self.fmin,
          self.fmax_loss,
          center=False,
      )
  
      return (
          mel.squeeze(),
          audio.squeeze(0),
          filename,
          mel_loss.squeeze(),
      )
  
imports: import math
  import os
  import random
  import torch
  import torch.utils.data
  import numpy
  from librosa.util import normalize
  from scipy.io.wavfile import read
  from librosa.filters import mel
label_desc: Use image node_id 2 for calling the MelDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: mel,  audio,  filename,  mel_loss