class_docstr: Construct a SqueezeBERT tokenizer. Based on WordPiece.
  
  This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
  this superclass for more information regarding those methods.
  
  Args:
      vocab_file (`str`):
          File containing the vocabulary.
      do_lower_case (`bool`, *optional*, defaults to `True`):
          Whether or not to lowercase the input when tokenizing.
      do_basic_tokenize (`bool`, *optional*, defaults to `True`):
          Whether or not to do basic tokenization before WordPiece.
      never_split (`Iterable`, *optional*):
          Collection of tokens which will never be split during tokenization. Only has an effect when
          `do_basic_tokenize=True`
      unk_token (`str`, *optional*, defaults to `"[UNK]"`):
          The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
          token instead.
      sep_token (`str`, *optional*, defaults to `"[SEP]"`):
          The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
          sequence classification or for a text and a question for question answering. It is also used as the last
          token of a sequence built with special tokens.
      pad_token (`str`, *optional*, defaults to `"[PAD]"`):
          The token used for padding, for example when batching sequences of different lengths.
      cls_token (`str`, *optional*, defaults to `"[CLS]"`):
          The classifier token which is used when doing sequence classification (classification of the whole sequence
          instead of per-token classification). It is the first token of the sequence when built with special tokens.
      mask_token (`str`, *optional*, defaults to `"[MASK]"`):
          The token used for masking values. This is the token used when training this model with masked language
          modeling. This is the token which the model will try to predict.
      tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
          Whether or not to tokenize Chinese characters.
  
          This should likely be deactivated for Japanese (see this
          [issue](https://github.com/huggingface/transformers/issues/328)).
      strip_accents (`bool`, *optional*):
          Whether or not to strip all accents. If this option is not specified, then it will be determined by the
          value for `lowercase` (as in the original SqueezeBERT).
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docstr: Converts a sequence of tokens (string) in a single string.
docstr_tok: ["Converts","a","sequence","of","tokens","(","string",")","in","a","single","string","."]
code: def convert_tokens_to_string(self, tokens):
      """Converts a sequence of tokens (string) in a single string."""
      out_string = " ".join(tokens).replace(" ##", "").strip()
      return out_string
  
imports: import collections
  import os
  import unicodedata
  from typing import List, Optional, Tuple
  from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
  from ...utils import logging
label_desc: Use image node_id 8 for calling the SqueezeBertTokenizer obj's underlying member method code with example usage: obj.convert_tokens_to_string(tokens) and returns: out_string