code: def parse(self, text: str, normalize=False) -> 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)
          print("changed to one")
  
      with eval_phon_mode:
          tokens = self.tokenizer.encode(text)
      print("text to token phone_prob")
  
      return (
          torch.tensor(tokens)
          .long()
          .unsqueeze(0)
          .cuda()
          .to(self.device)
      )
  
imports: import contextlib
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from pytorch_lightning.loggers import TensorBoardLogger
  from nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers import BaseTokenizer
  from nemo.collections.tts.losses.radttsloss import AttentionBinarizationLoss, RADTTSLoss
  from nemo.collections.tts.models.base import SpectrogramGenerator
  from nemo.collections.tts.parts.utils.helpers import batch_from_ragged, g2p_backward_compatible_support, plot_alignment_to_numpy, regulate_len, sample_tts_input
  from nemo.core.classes import Exportable
  from nemo.core.classes.common import typecheck
  from nemo.core.neural_types.elements import Index, MelSpectrogramType, RegressionValuesType, TokenDurationType, TokenIndex
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.core.optim.radam import RAdam
  from nemo.utils import logging
  from nemo.utils.decorators import experimental
label_desc: Use image node_id 15 for calling the RadTTSModel obj's underlying member method code with example usage: obj.parse(text, normalize) and returns: torch