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 _decode(
      self,
      token_ids: Union[int, List[int]],
      skip_special_tokens: bool = False,
      clean_up_tokenization_spaces: bool = None,
      **kwargs,
  ) -> str:
      self._decode_use_source_tokenizer = kwargs.pop(
          "use_source_tokenizer", False
      )
  
      if isinstance(token_ids, int):
          token_ids = [token_ids]
      text = self._tokenizer.decode(
          token_ids, skip_special_tokens=skip_special_tokens
      )
  
      clean_up_tokenization_spaces = (
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
          clean_text = self.clean_up_tokenization(text)
          return clean_text
      else:
          return text
  
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 25 for calling the PreTrainedTokenizerFast obj's underlying member method code with example usage: obj._decode(token_ids, skip_special_tokens, clean_up_tokenization_spaces) and returns: clean_text, text