class_docstr: This is a classification model that selects the best heteronym option out of possible dictionary entries.
  Supports only heteronyms, no OOV.
docstr: This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
  Returns:
      List of available pre-trained models.
docstr_tok: ["This","method","returns","a","list","of","pre-trained","model","which","can","be","instantiated","directly","from","NVIDIA","'s","NGC","cloud",".","Returns",":","List","of","available","pre-trained","models","."]
code: def list_available_models(cls) -> Optional[PretrainedModelInfo]:
      """
      This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
      Returns:
          List of available pre-trained models.
      """
      return []
  
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 20 for calling the HeteronymClassificationModel obj's underlying member method code with example usage: obj.list_available_models(cls) and returns: list