class_docstr: Base class for encoder decoder RNNT-based models with auxiliary CTC decoder/loss and subword tokenization.
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.
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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.
      """
      results = []
      return results
  
imports: import copy
  import os
  from typing import Dict, Optional, Union
  import torch
  from omegaconf import DictConfig, ListConfig, OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from nemo.collections.asr.losses.ctc import CTCLoss
  from nemo.collections.asr.losses.rnnt import RNNTLoss
  from nemo.collections.asr.metrics.wer import WER
  from nemo.collections.asr.parts.mixins import ASRBPEMixin
  from nemo.collections.asr.parts.submodules.ctc_decoding import CTCBPEDecoding, CTCBPEDecodingConfig
  from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTBPEDecoding, RNNTBPEDecodingConfig
  from nemo.collections.multimodal.speech_cv.data import video_to_text_dataset
  from nemo.collections.multimodal.speech_cv.models.visual_hybrid_rnnt_ctc_models import VisualEncDecHybridRNNTCTCModel
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 6 for calling the VisualEncDecHybridRNNTCTCBPEModel obj's underlying member method code with example usage: obj.list_available_models(cls) and returns: results