class_docstr: Encoder decoder models with Byte Pair Encoding and various lattice losses.
class_docstr_tok: ["Encoder","decoder","models","with","Byte","Pair","Encoding","and","various","lattice","losses","."]
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.
      """
      pass
  
imports: from typing import List, Optional
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from nemo.collections.asr.models.ctc_bpe_models import EncDecCTCModelBPE
  from nemo.collections.asr.models.ctc_models import EncDecCTCModel
  from nemo.collections.asr.models.rnnt_bpe_models import EncDecRNNTBPEModel
  from nemo.collections.asr.models.rnnt_models import EncDecRNNTModel
  from nemo.collections.asr.parts.k2.classes import ASRK2Mixin
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the EncDecK2RnntSeqModelBPE obj's underlying member method code with example usage: obj.list_available_models(cls) without return types