class_docstr: Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).
  
  Inherits from [`~tokenization_utils_base.PreTrainedTokenizerBase`].
  
  Handles all the shared methods for tokenization and special tokens, as well as methods for
  downloading/caching/loading pretrained tokenizers, as well as adding tokens to the vocabulary.
  
  This class also contains the added tokens in a unified way on top of all tokenizers so we don't have to handle the
  specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...).
code: def convert_tokens_to_string(self, tokens: List[str]) -> str:
      return self.backend_tokenizer.decoder.decode(tokens)
  
imports: import copy
  import json
  import os
  from collections import defaultdict
  from typing import Any, Dict, List, Optional, Tuple, Union
  import tokenizers.pre_tokenizers
  from tokenizers import Encoding
  from tokenizers import Tokenizer
  from tokenizers.decoders import Decoder
  from tokenizers.trainers import BpeTrainer, UnigramTrainer, WordLevelTrainer, WordPieceTrainer
  from .convert_slow_tokenizer import convert_slow_tokenizer
  from .tokenization_utils import PreTrainedTokenizer
  from .tokenization_utils_base import INIT_TOKENIZER_DOCSTRING, AddedToken, BatchEncoding, PreTokenizedInput, PreTokenizedInputPair, PreTrainedTokenizerBase, SpecialTokensMixin, TextInput, TextInputPair, TruncationStrategy
  from .utils import PaddingStrategy, add_end_docstrings, logging
label_desc: Use image node_id 24 for calling the PreTrainedTokenizerFast obj's underlying member method code with example usage: obj.convert_tokens_to_string(tokens) and returns: self