class_docstr: This class is a copy of `DeDLOC's tokenizer implementation <https://github.com/yandex-research/DeDLOC/blob/main/sahajbert/tokenizer/tokenizer_model.py>`__ .
  
  Custom SentencePiece Unigram Tokenizer with NMT, NKFC, spaces and lower-casing characters normalization
  Represents the Unigram algorithm, with the pretokenization used by SentencePiece
class_docstr_tok: ["This","class","is","a","copy","of","`","DeDLOC","'s","tokenizer","implementation","<","https",":","\/\/github.com\/yandex-research\/DeDLOC\/blob\/main\/sahajbert\/tokenizer\/tokenizer_model.py",">","`","__",".","Custom","SentencePiece","Unigram","Tokenizer","with","NMT",",","NKFC",",","spaces","and","lower-casing","characters","normalization","Represents","the","Unigram","algorithm",",","with","the","pretokenization","used","by","SentencePiece"]
code: def add_unk_id(self):
      tokenizer_json = json.loads(self._tokenizer.to_str())
  
      tokenizer_json["model"]["unk_id"] = self.special_tokens["unk"][
          "id"
      ]
  
      self._tokenizer = Tokenizer.from_str(json.dumps(tokenizer_json))
  
file_path: transformers/examples/flax/language-modeling/t5_tokenizer_model.py
imports: import json
  from typing import Iterator, List, Union
  from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
  from tokenizers.implementations.base_tokenizer import BaseTokenizer
  from tokenizers.models import Unigram
  from tokenizers.processors import TemplateProcessing
label_desc: Use image node_id 4 for calling the SentencePieceUnigramTokenizer obj's underlying member method code with example usage: obj.add_unk_id() without return types