class_docstr: Construct a LayoutLMv2 tokenizer. Based on WordPiece. [`LayoutLMv2Tokenizer`] can be used to turn words, word-level
  bounding boxes and optional word labels to token-level `input_ids`, `attention_mask`, `token_type_ids`, `bbox`, and
  optional `labels` (for token classification).
  
  This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
  this superclass for more information regarding those methods.
  
  [`LayoutLMv2Tokenizer`] runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the
  word-level bounding boxes into token-level bounding boxes.
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docstr: Converts an index (integer) in a token (str) using the vocab.
docstr_tok: ["Converts","an","index","(","integer",")","in","a","token","(","str",")","using","the","vocab","."]
code: def _convert_id_to_token(self, index):
      """Converts an index (integer) in a token (str) using the vocab."""
      return self.ids_to_tokens.get(index, self.unk_token)
  
imports: import collections
  import os
  import sys
  import unicodedata
  from typing import Dict, List, Optional, Tuple, Union
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
  from ...tokenization_utils_base import BatchEncoding, EncodedInput, PreTokenizedInput, TextInput, TextInputPair, TruncationStrategy
  from ...utils import PaddingStrategy, TensorType, add_end_docstrings, logging
label_desc: Use image node_id 7 for calling the LayoutLMv2Tokenizer obj's underlying member method code with example usage: obj._convert_id_to_token(index) and returns: self