class_docstr: This is a classification model that selects the best heteronym option out of possible dictionary entries.
  Supports only heteronyms, no OOV.
code: def set_wordid_to_phonemes(self, wordid_to_phonemes_file: str):
      if wordid_to_phonemes_file is None or not os.path.exists(
          wordid_to_phonemes_file
      ):
          logging.warning(
              f"{wordid_to_phonemes_file} not found, skip setting wordid_to_phonemes."
          )
      else:
          self.wordid_to_phonemes_file = wordid_to_phonemes_file
          self.wordid_to_phonemes = get_wordid_to_phonemes(
              self.wordid_to_phonemes_file
          )
          logging.info(
              f"Wordid to phonemes file is set to {wordid_to_phonemes_file}"
          )
  
imports: import json
  import os
  from typing import List, Optional
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from nemo.collections.common.losses import CrossEntropyLoss
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.modules.common import TokenClassifier
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.collections.tts.g2p.data.heteronym_classification import HeteronymClassificationDataset
  from nemo.collections.tts.g2p.utils import get_heteronym_spans, get_wordid_to_phonemes, read_wordids
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 10 for calling the HeteronymClassificationModel obj's underlying member method code with example usage: obj.set_wordid_to_phonemes(wordid_to_phonemes_file) without return types