code: def forward(self, mel_spec, lengths=None):
      mel_spec = torch.exp(mel_spec)
      mel_spec = 20 * torch.log10(torch.clamp(mel_spec, min=1e-5))
      if self._min_level_db is not None:
          mel_spec = (
              self._min_level_db - mel_spec
          ) / self._min_level_db
          mel_spec = torch.clamp(mel_spec, min=0, max=1)
      waveform, lengths = self._model.infer(mel_spec, lengths)
      waveform = utils._unnormalize_waveform(
          waveform, self._model.n_bits
      )
      waveform = mu_law_decoding(waveform, self._model.n_classes)
      waveform = waveform.squeeze(1)
      return waveform, 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 _WaveRNNVocoder obj's underlying member method code with example usage: obj.forward(mel_spec, lengths) and returns: waveform,  lengths