class_docstr: Transformer-based (duplex) decoder model for TN/ITN.
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.
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.
      """
      result = []
      result.append(
          PretrainedModelInfo(
              pretrained_model_name="neural_text_normalization_t5",
              location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/neural_text_normalization_t5/versions/1.5.0/files/neural_text_normalization_t5_decoder.nemo",
              description="Text Normalization model's decoder model.",
          )
      )
      result.append(
          PretrainedModelInfo(
              pretrained_model_name="itn_en_t5",
              location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/itn_en_t5/versions/1.11.0/files/itn_en_t5_decoder.nemo",
              description="English Inverse Text Normalization model's decoder model.",
          )
      )
      return result
  
imports: import json
  import os
  from collections import defaultdict
  from math import ceil
  from typing import Dict, List, Optional, Union
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, DataCollatorForSeq2Seq
  import nemo.collections.nlp.data.text_normalization.constants
  from nemo.collections.common.tokenizers.moses_tokenizers import MosesProcessor
  from nemo.collections.nlp.data.text_normalization import TextNormalizationTestDataset
  from nemo.collections.nlp.data.text_normalization.decoder_dataset import TarredTextNormalizationDecoderDataset, TextNormalizationDecoderDataset
  from nemo.collections.nlp.models.duplex_text_normalization.utils import get_formatted_string
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types import ChannelType, LabelsType, LossType, MaskType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 19 for calling the DuplexDecoderModel obj's underlying member method code with example usage: obj.list_available_models(cls) and returns: result