class_docstr: Transformer-based (duplex) tagger model for TN/ITN.
docstr: Lightning calls this inside the test loop with the data from the test dataloader
  passed in as `batch`.
docstr_tok: ["Lightning","calls","this","inside","the","test","loop","with","the","data","from","the","test","dataloader","passed","in","as","`","batch","`","."]
code: def test_step(self, batch, batch_idx):
      """
      Lightning calls this inside the test loop with the data from the test dataloader
      passed in as `batch`.
      """
      return self.validation_step(batch, batch_idx)
  
imports: from time import perf_counter
  from typing import Dict, List, Optional
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from torch import nn
  from transformers import AutoModelForTokenClassification, AutoTokenizer, DataCollatorForTokenClassification
  from transformers.tokenization_utils_base import BatchEncoding
  from nemo.collections.nlp.data.text_normalization import TextNormalizationTaggerDataset, constants
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.models.duplex_text_normalization.utils import has_numbers
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types import ChannelType, LogitsType, MaskType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 10 for calling the DuplexTaggerModel obj's underlying member method code with example usage: obj.test_step(batch, batch_idx) and returns: self