class_docstr: DuplexTextNormalizationModel is a wrapper class that can be used to
  encapsulate a trained tagger and a trained decoder. The class is intended
  to be used for inference only (e.g., for evaluation).
class_docstr_tok: ["DuplexTextNormalizationModel","is","a","wrapper","class","that","can","be","used","to","encapsulate","a","trained","tagger","and","a","trained","decoder",".","The","class","is","intended","to","be","used","for","inference","only","(","e.g.",",","for","evaluation",")","."]
code: def __init__(self, tagger, decoder, lang):
      super(DuplexTextNormalizationModel, self).__init__()
  
      self.tagger = tagger
      self.decoder = decoder
      self.lang = lang
  
file_path: NeMo/nemo/collections/nlp/models/duplex_text_normalization/duplex_tn.py
imports: from math import ceil
  from time import perf_counter
  from typing import List
  import numpy
  import torch.nn
  from tqdm import tqdm
  from nemo.collections.nlp.data.text_normalization import TextNormalizationTestDataset, constants
  from nemo.collections.nlp.data.text_normalization.utils import input_preprocessing
  from nemo.collections.nlp.models.duplex_text_normalization.utils import get_formatted_string
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new DuplexTextNormalizationModel object from inherited base classes: nn with example: obj = DuplexTextNormalizationModel(tagger, decoder, lang)