class_docstr: Base class inherited by all tokenizer classes. This class
  handles downloading and loading pre-trained tokenizer and adding tokens to
  the vocabulary.
  
  Derived class can set up a few special tokens to be used in common scripts
  and internals: :attr:`bos_token`, :attr:`eos_token`, :attr:`unk_token`,
  :attr:`sep_token`, :attr:`pad_token`, :attr:`cls_token`,
  :attr:`mask_token`, and :attr:`additional_special_tokens`.
  
  We defined an :attr:`added_tokens_encoder` to add new tokens to the
  vocabulary without having to handle the specific vocabulary augmentation
  methods of the various underlying dictionary structures (`BPE`,
  `sentencepiece` ...).
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docstr: Maps a sequence of ids (integer) to a string, using the
  tokenizer and vocabulary with options to remove special tokens and
  clean up tokenization spaces.
  
  Args:
      token_ids: A list of token ids.
      skip_special_tokens: Whether to skip the special tokens.
      clean_up_tokenization_spaces: Whether to clean up a list of simple
          English tokenization artifacts like spaces before punctuations
          and abbreviated forms.
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code: def map_id_to_text(
      self,
      token_ids: List[int],
      skip_special_tokens: bool = False,
      clean_up_tokenization_spaces: bool = True,
  ) -> str:
      r"""Maps a sequence of ids (integer) to a string, using the
      tokenizer and vocabulary with options to remove special tokens and
      clean up tokenization spaces.
  
      Args:
          token_ids: A list of token ids.
          skip_special_tokens: Whether to skip the special tokens.
          clean_up_tokenization_spaces: Whether to clean up a list of simple
              English tokenization artifacts like spaces before punctuations
              and abbreviated forms.
      """
      filtered_tokens = self.map_id_to_token(
          token_ids, skip_special_tokens=skip_special_tokens
      )
      text = self.map_token_to_text(filtered_tokens)
      if clean_up_tokenization_spaces:
          text = self.clean_up_tokenization(text)
      return text
  
imports: from typing import Any, Dict, List, Optional, Tuple, overload
  import json
  import os
  import warnings
  from texar.tf.module_base import ModuleBase
label_desc: Use image node_id 22 for calling the TokenizerBase obj's underlying member method code with example usage: obj.map_id_to_text(token_ids, skip_special_tokens, clean_up_tokenization_spaces) and returns: text