class_docstr: Transformer-based (duplex) decoder model for TN/ITN.
code: def setup_test_data(self, test_data_config: Optional[DictConfig]):
      if not test_data_config or test_data_config.data_path is None:
          logging.info(
              f"Dataloader config or file_path for the test is missing, so no data loader for test is created!"
          )
          self.test_dataset, self._test_dl = None, None
          return
      (
          self.test_dataset,
          self._test_dl,
      ) = self._setup_dataloader_from_config(
          cfg=test_data_config, data_split="test"
      )
  
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 17 for calling the DuplexDecoderModel obj's underlying member method code with example usage: obj.setup_test_data(test_data_config) without return types