class_docstr: GAN Dataset searchs for all the wav files under root path
  and converts them to acoustic features on the fly and returns
  random segments of (audio, feature) couples.
docstr: load (audio, feat) couple 
docstr_tok: ["load","(","audio",",","feat",")","couple"]
code: def load_item(self, idx):
      """load (audio, feat) couple"""
      if self.compute_feat:
          # compute features from wav
          wavpath = self.item_list[idx]
          # print(wavpath)
  
          if self.use_cache and self.cache[idx] is not None:
              audio, mel = self.cache[idx]
          else:
              audio = self.ap.load_wav(wavpath)
  
              if len(audio) < self.seq_len + self.pad_short:
                  audio = np.pad(
                      audio,
                      (0, self.seq_len + self.pad_short - len(audio)),
                      mode="constant",
                      constant_values=0.0,
                  )
  
              mel = self.ap.melspectrogram(audio)
      else:
          # load precomputed features
          wavpath, feat_path = self.item_list[idx]
  
          if self.use_cache and self.cache[idx] is not None:
              audio, mel = self.cache[idx]
          else:
              audio = self.ap.load_wav(wavpath)
              mel = np.load(feat_path)
  
      # correct the audio length wrt padding applied in stft
      audio = np.pad(audio, (0, self.hop_len), mode="edge")
      audio = audio[: mel.shape[-1] * self.hop_len]
      assert (
          mel.shape[-1] * self.hop_len == audio.shape[-1]
      ), f" [!] {mel.shape[-1] * self.hop_len} vs {audio.shape[-1]}"
  
      audio = torch.from_numpy(audio).float().unsqueeze(0)
      mel = torch.from_numpy(mel).float().squeeze(0)
  
      if self.return_segments:
          max_mel_start = mel.shape[1] - self.feat_frame_len
          mel_start = random.randint(0, max_mel_start)
          mel_end = mel_start + self.feat_frame_len
          mel = mel[:, mel_start:mel_end]
  
          audio_start = mel_start * self.hop_len
          audio = audio[:, audio_start : audio_start + self.seq_len]
  
      if (
          self.use_noise_augment
          and self.is_training
          and self.return_segments
      ):
          audio = audio + (1 / 32768) * torch.randn_like(audio)
      return (mel, audio)
  
file_path: TTS/TTS/vocoder/datasets/gan_dataset.py
imports: import os
  import glob
  import torch
  import random
  import numpy
  from torch.utils.data import Dataset
  from multiprocessing import Manager
label_desc: Use image node_id 7 for calling the GANDataset obj's underlying member method code with example usage: obj.load_item(idx) and returns: mel,  audio