code: def forward(
      self,
      tokens,
      speakers=None,
      noise_scale=1,
      length_scale=1,
      noise_scale_w=1.0,
      max_len=1000,
  ):
      text_len = (
          torch.tensor([tokens.size(-1)]).to(int).to(tokens.device)
      )
      (
          audio_pred,
          attn,
          y_mask,
          (z, z_p, m_p, logs_p),
      ) = self.net_g.infer(
          tokens,
          text_len,
          speakers=speakers,
          noise_scale=noise_scale,
          length_scale=length_scale,
          noise_scale_w=noise_scale_w,
          max_len=max_len,
      )
      return audio_pred, attn, y_mask, (z, z_p, m_p, logs_p)
  
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 6 for calling the VitsModel obj's underlying member method code with example usage: obj.forward(tokens, speakers, noise_scale, length_scale, noise_scale_w, max_len) and returns: audio_pred,  attn,  y_mask,  z,  z_p,  m_p,  logs_p