class_docstr: WaveGrad 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.
class_docstr_tok: ["WaveGrad","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: This is used in tune_wavegrad.py.
  It pads sequences to the max length.
docstr_tok: ["This","is","used","in","tune_wavegrad.py",".","It","pads","sequences","to","the","max","length","."]
code: def collate_full_clips(batch):
      """This is used in tune_wavegrad.py.
      It pads sequences to the max length."""
      max_mel_length = (
          max([b[0].shape[1] for b in batch])
          if len(batch) > 1
          else batch[0][0].shape[1]
      )
      max_audio_length = (
          max([b[1].shape[0] for b in batch])
          if len(batch) > 1
          else batch[0][1].shape[0]
      )
  
      mels = torch.zeros(
          [len(batch), batch[0][0].shape[0], max_mel_length]
      )
      audios = torch.zeros([len(batch), max_audio_length])
  
      for idx, b in enumerate(batch):
          mel = b[0]
          audio = b[1]
          mels[idx, :, : mel.shape[1]] = mel
          audios[idx, : audio.shape[0]] = audio
  
      return mels, audios
  
file_path: TTS/TTS/vocoder/datasets/wavegrad_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 8 for calling the WaveGradDataset obj's underlying member method code with example usage: obj.collate_full_clips(batch) and returns: mels,  audios