code: def forward(self, mel_spec, lengths=None):
      mel_spec = torch.exp(mel_spec)
      mel_spec = mel_spec.clone().detach().requires_grad_(True)
      spec = self._inv_mel(mel_spec)
      spec = spec.detach().requires_grad_(False)
      waveforms = self._griffin_lim(spec)
      return waveforms, lengths
  
imports: import re
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torchaudio._internal import load_state_dict_from_url
  from torchaudio.functional import mu_law_decoding
  from torchaudio.models import Tacotron2, WaveRNN
  from torchaudio.transforms import GriffinLim, InverseMelScale
  from .None import utils
  from .interface import Tacotron2TTSBundle
label_desc: Use image node_id 3 for calling the _GriffinLimVocoder obj's underlying member method code with example usage: obj.forward(mel_spec, lengths) and returns: waveforms,  lengths