class_docstr: WaveRNN Dataset searchs for all the wav files under root path
  and converts them to acoustic features on the fly.
docstr: load (audio, feat) couple if feature_path is set
  else compute it on the fly
code: def load_item(self, index):
      """
      load (audio, feat) couple if feature_path is set
      else compute it on the fly
      """
      if self.compute_feat:
          wavpath = self.item_list[index]
          audio = self.ap.load_wav(wavpath)
          min_audio_len = 2 * self.seq_len + (
              2 * self.pad * self.hop_len
          )
          if audio.shape[0] < min_audio_len:
              print(" [!] Instance is too short! : {}".format(wavpath))
              audio = np.pad(
                  audio,
                  [0, min_audio_len - audio.shape[0] + self.hop_len],
              )
          mel = self.ap.melspectrogram(audio)
  
          if self.mode in ["gauss", "mold"]:
              x_input = audio
          elif isinstance(self.mode, int):
              x_input = (
                  self.ap.mulaw_encode(audio, qc=self.mode)
                  if self.mulaw
                  else self.ap.quantize(audio, bits=self.mode)
              )
          else:
              raise RuntimeError("Unknown dataset mode - ", self.mode)
  
      else:
          wavpath, feat_path = self.item_list[index]
          mel = np.load(feat_path.replace("/quant/", "/mel/"))
  
          if mel.shape[-1] < self.mel_len + 2 * self.pad:
              print(" [!] Instance is too short! : {}".format(wavpath))
              self.item_list[index] = self.item_list[index + 1]
              feat_path = self.item_list[index]
              mel = np.load(feat_path.replace("/quant/", "/mel/"))
          if self.mode in ["gauss", "mold"]:
              x_input = self.ap.load_wav(wavpath)
          elif isinstance(self.mode, int):
              x_input = np.load(feat_path.replace("/mel/", "/quant/"))
          else:
              raise RuntimeError("Unknown dataset mode - ", self.mode)
  
      return mel, x_input, wavpath
  
file_path: TTS/TTS/vocoder/datasets/wavernn_dataset.py
imports: import torch
  import numpy
  from torch.utils.data import Dataset
label_desc: Use image node_id 4 for calling the WaveRNNDataset obj's underlying member method code with example usage: obj.load_item(index) and returns: mel,  x_input,  wavpath