class_docstr: Base class for encoder decoder CTC-based models.
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) -> List[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 = []
  
      model = PretrainedModelInfo(
          pretrained_model_name="QuartzNet15x5Base-En",
          description="QuartzNet15x5 model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1 for 600 epochs. The model achieves a WER of 3.79% on LibriSpeech dev-clean, and a WER of 10.05% on dev-other. Please visit https://ngc.nvidia.com/catalog/models/nvidia:nemospeechmodels for further details.",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/QuartzNet15x5Base-En.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="stt_en_quartznet15x5",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_quartznet15x5",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_quartznet15x5/versions/1.0.0rc1/files/stt_en_quartznet15x5.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="stt_en_jasper10x5dr",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_jasper10x5dr",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_jasper10x5dr/versions/1.0.0rc1/files/stt_en_jasper10x5dr.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="stt_ca_quartznet15x5",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ca_quartznet15x5",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ca_quartznet15x5/versions/1.0.0rc1/files/stt_ca_quartznet15x5.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="stt_it_quartznet15x5",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_quartznet15x5",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_it_quartznet15x5/versions/1.0.0rc1/files/stt_it_quartznet15x5.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="stt_fr_quartznet15x5",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_quartznet15x5",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_quartznet15x5/versions/1.0.0rc1/files/stt_fr_quartznet15x5.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="stt_es_quartznet15x5",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_quartznet15x5",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_quartznet15x5/versions/1.0.0rc1/files/stt_es_quartznet15x5.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="stt_de_quartznet15x5",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_quartznet15x5",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_quartznet15x5/versions/1.0.0rc1/files/stt_de_quartznet15x5.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="stt_pl_quartznet15x5",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_pl_quartznet15x5",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_pl_quartznet15x5/versions/1.0.0rc1/files/stt_pl_quartznet15x5.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="stt_ru_quartznet15x5",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_quartznet15x5",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ru_quartznet15x5/versions/1.0.0rc1/files/stt_ru_quartznet15x5.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="stt_zh_citrinet_512",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_512",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_512/versions/1.0.0rc1/files/stt_zh_citrinet_512.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="stt_zh_citrinet_1024_gamma_0_25",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_1024_gamma_0_25",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_zh_citrinet_1024_gamma_0_25.nemo",
      )
  
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="stt_zh_citrinet_1024_gamma_0_25",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_1024_gamma_0_25",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_zh_citrinet_1024_gamma_0_25.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="asr_talknet_aligner",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:asr_talknet_aligner",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/asr_talknet_aligner/versions/1.0.0rc1/files/qn5x5_libri_tts_phonemes.nemo",
      )
      results.append(model)
  
      return results
  
imports: import copy
  import json
  import os
  import tempfile
  from math import ceil
  from typing import Dict, List, Optional, Union
  import torch
  from omegaconf import DictConfig, OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from tqdm.auto import tqdm
  from nemo.collections.asr.data import audio_to_text_dataset
  from nemo.collections.asr.data.audio_to_text_dali import AudioToCharDALIDataset, DALIOutputs
  from nemo.collections.asr.data.audio_to_text_lhotse import LhotseSpeechToTextBpeDataset
  from nemo.collections.asr.losses.ctc import CTCLoss
  from nemo.collections.asr.metrics.wer import WER
  from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel
  from nemo.collections.asr.parts.mixins import ASRModuleMixin, InterCTCMixin
  from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecoding, CTCDecodingConfig
  from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType
  from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config
  from nemo.collections.common.parts.preprocessing.parsers import make_parser
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.classes.mixins import AccessMixin
  from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, LogprobsType, NeuralType, SpectrogramType
  from nemo.utils import logging
label_desc: Use image node_id 21 for calling the EncDecCTCModel obj's underlying member method code with example usage: obj.list_available_models(cls) and returns: results