class_docstr: VITS module (generator + discriminator).
  
  This is a module of VITS described in `Conditional Variational Autoencoder
  with Adversarial Learning for End-to-End Text-to-Speech`_.
  
  .. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End
      Text-to-Speech`: https://arxiv.org/abs/2006.04558
class_docstr_tok: ["VITS","module","(","generator","+","discriminator",")",".","This","is","a","module","of","VITS","described","in","`","Conditional","Variational","Autoencoder","with","Adversarial","Learning","for","End-to-End","Text-to-Speech","`","_",".","..","_","`","Conditional","Variational","Autoencoder","with","Adversarial","Learning","for","End-to-End","Text-to-Speech","`",":","https",":","\/\/arxiv.org\/abs\/2006.04558"]
docstr: Return whether or not vocoder is required.
docstr_tok: ["Return","whether","or","not","vocoder","is","required","."]
code: def require_vocoder(self):
      """Return whether or not vocoder is required."""
      return False
  
imports: from contextlib import contextmanager
  from distutils.version import LooseVersion
  from typing import Any, Dict, Optional
  import torch
  from typeguard import check_argument_types
  from espnet2.gan_tts.abs_gan_tts import AbsGANTTS
  from espnet2.gan_tts.hifigan import HiFiGANMultiPeriodDiscriminator, HiFiGANMultiScaleDiscriminator, HiFiGANMultiScaleMultiPeriodDiscriminator, HiFiGANPeriodDiscriminator, HiFiGANScaleDiscriminator
  from espnet2.gan_tts.hifigan.loss import DiscriminatorAdversarialLoss, FeatureMatchLoss, GeneratorAdversarialLoss, MelSpectrogramLoss
  from espnet2.gan_tts.utils import get_segments
  from espnet2.gan_tts.vits.generator import VITSGenerator
  from espnet2.gan_tts.vits.loss import KLDivergenceLoss
  from espnet2.torch_utils.device_funcs import force_gatherable
label_desc: Use image node_id 3 for calling the VITS obj's underlying member method code with example usage: obj.require_vocoder() and returns: False