class_docstr: Construct a "fast" LayoutLM tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
  
  This tokenizer inherits from [`PreTrainedTokenizerFast`] 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.
      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.
      clean_text (`bool`, *optional*, defaults to `True`):
          Whether or not to clean the text before tokenization by removing any control characters and replacing all
          whitespaces by the classic one.
      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 LayoutLM).
      wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
          The prefix for subwords.
docstr: Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
  adding special tokens. A LayoutLM sequence has the following format:
  
  - single sequence: `[CLS] X [SEP]`
  - pair of sequences: `[CLS] A [SEP] B [SEP]`
  
  Args:
      token_ids_0 (`List[int]`):
          List of IDs to which the special tokens will be added.
      token_ids_1 (`List[int]`, *optional*):
          Optional second list of IDs for sequence pairs.
  
  Returns:
      `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
code: def build_inputs_with_special_tokens(
      self, token_ids_0, token_ids_1=None
  ):
      """
      Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
      adding special tokens. A LayoutLM sequence has the following format:
  
      - single sequence: `[CLS] X [SEP]`
      - pair of sequences: `[CLS] A [SEP] B [SEP]`
  
      Args:
          token_ids_0 (`List[int]`):
              List of IDs to which the special tokens will be added.
          token_ids_1 (`List[int]`, *optional*):
              Optional second list of IDs for sequence pairs.
  
      Returns:
          `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
      """
      output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
  
      if token_ids_1:
          output += token_ids_1 + [self.sep_token_id]
  
      return output
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py
imports: import json
  from typing import List, Optional, Tuple
  from tokenizers import normalizers
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_layoutlm import LayoutLMTokenizer
label_desc: Use image node_id 2 for calling the LayoutLMTokenizerFast obj's underlying member method code with example usage: obj.build_inputs_with_special_tokens(token_ids_0, token_ids_1) and returns: output