code: def validation_step(self, batch, batch_idx):
      speakers = None
      if self.cfg.n_speakers > 1:
          (audio, audio_len, text, text_len, speakers) = batch
      else:
          (audio, audio_len, text, text_len) = batch
  
      audio_pred, _, mask, *_ = self.net_g.infer(
          text, text_len, speakers, max_len=1000
      )
  
      audio_pred = audio_pred.squeeze()
      audio_pred_len = (
          mask.sum([1, 2]).long()
          * self._cfg.validation_ds.dataset.hop_length
      )
  
      mel, mel_lengths = self.audio_to_melspec_processor(
          audio, audio_len
      )
      (
          audio_pred_mel,
          audio_pred_mel_len,
      ) = self.audio_to_melspec_processor(audio_pred, audio_pred_len)
  
      # plot audio once per epoch
      if (
          batch_idx == 0
          and isinstance(self.logger, WandbLogger)
          and HAVE_WANDB
      ):
          logger = self.logger.experiment
  
          specs = []
          audios = []
          specs += [
              wandb.Image(
                  plot_spectrogram_to_numpy(
                      mel[0, :, : mel_lengths[0]].data.cpu().numpy()
                  ),
                  caption=f"val_mel_target",
              ),
              wandb.Image(
                  plot_spectrogram_to_numpy(
                      audio_pred_mel[0, :, : audio_pred_mel_len[0]]
                      .data.cpu()
                      .numpy()
                  ),
                  caption=f"val_mel_predicted",
              ),
          ]
  
          audios += [
              wandb.Audio(
                  audio[0, : audio_len[0]]
                  .data.cpu()
                  .to(torch.float)
                  .numpy(),
                  caption=f"val_wav_target",
                  sample_rate=self._cfg.sample_rate,
              ),
              wandb.Audio(
                  audio_pred[0, : audio_pred_len[0]]
                  .data.cpu()
                  .to(torch.float)
                  .numpy(),
                  caption=f"val_wav_predicted",
                  sample_rate=self._cfg.sample_rate,
              ),
          ]
  
          logger.log({"specs": specs, "audios": audios})
  
imports: import contextlib
  import omegaconf
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from pytorch_lightning.loggers import WandbLogger
  from torch.cuda.amp import autocast
  from torch.nn import functional
  from nemo.collections.tts.data.dataset import DistributedBucketSampler
  from nemo.collections.tts.losses.vits_losses import DiscriminatorLoss, FeatureMatchingLoss, GeneratorLoss, KlLoss
  from nemo.collections.tts.models.base import TextToWaveform
  from nemo.collections.tts.modules.vits_modules import MultiPeriodDiscriminator
  from nemo.collections.tts.parts.utils.helpers import clip_grad_value_, g2p_backward_compatible_support, plot_spectrogram_to_numpy, slice_segments
  from nemo.collections.tts.torch.tts_data_types import SpeakerID
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types.elements import AudioSignal, FloatType, Index, IntType, TokenIndex
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.core.optim.lr_scheduler import CosineAnnealing
  from nemo.utils import logging, model_utils
  from nemo.utils.decorators.experimental import experimental
label_desc: Use image node_id 8 for calling the VitsModel obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) without return types