code: def parse(self, text: str, normalize=True) -> torch.tensor:
      if self.training:
          logging.warning("parse() is meant to be called in eval mode.")
      if normalize and self.text_normalizer_call is not None:
          text = self.text_normalizer_call(
              text, **self.text_normalizer_call_kwargs
          )
  
      eval_phon_mode = contextlib.nullcontext()
      if hasattr(self.tokenizer, "set_phone_prob"):
          eval_phon_mode = self.tokenizer.set_phone_prob(prob=1.0)
  
      with eval_phon_mode:
          tokens = self.tokenizer.encode(text)
  
      return torch.tensor(tokens).long().unsqueeze(0).to(self.device)
  
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 4 for calling the VitsModel obj's underlying member method code with example usage: obj.parse(text, normalize) and returns: torch