class_docstr: Transformer-based (duplex) tagger model for TN/ITN.
docstr: Generates input examples for tracing etc.
  Returns:
      A tuple of input examples.
docstr_tok: ["Generates","input","examples","for","tracing","etc",".","Returns",":","A","tuple","of","input","examples","."]
code: def input_example(self):
      """
      Generates input examples for tracing etc.
      Returns:
          A tuple of input examples.
      """
      sample = next(self.parameters())
      input_ids = torch.randint(
          low=0, high=2048, size=(2, 16), device=sample.device
      )
      attention_mask = torch.randint(
          low=0, high=1, size=(2, 16), device=sample.device
      )
      return tuple([input_ids, attention_mask])
  
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 19 for calling the DuplexTaggerModel obj's underlying member method code with example usage: obj.input_example() and returns: tuple