class_docstr: Transformer-based (duplex) decoder model for TN/ITN.
code: def output_types(self) -> Optional[Dict[str, NeuralType]]:
      return {"loss": NeuralType((), LossType())}
  
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 2 for calling the DuplexDecoderModel obj's underlying member method code with example usage: obj.output_types() and returns: dict