class_docstr: Construct a TAPEX tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).
  
  This tokenizer can be used to flatten one or more table(s) and concatenate them with one or more related sentences
  to be used by TAPEX models. The format that the TAPEX tokenizer creates is the following:
  
  sentence col: col1 | col2 | col 3 row 1 : val1 | val2 | val3 row 2 : ...
  
  The tokenizer supports a single table + single query, a single table and multiple queries (in which case the table
  will be duplicated for every query), a single query and multiple tables (in which case the query will be duplicated
  for every table), and multiple tables and queries. In other words, you can provide a batch of tables + questions to
  the tokenizer for instance to prepare them for the model.
  
  Tokenization itself is based on the BPE algorithm. It is identical to the one used by BART, RoBERTa and GPT-2.
  
  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`):
          Path to the vocabulary file.
      merges_file (`str`):
          Path to the merges file.
      do_lower_case (`bool`, *optional*, defaults to `True`):
          Whether or not to lowercase the input when tokenizing.
      errors (`str`, *optional*, defaults to `"replace"`):
          Paradigm to follow when decoding bytes to UTF-8. See
          [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
      bos_token (`str`, *optional*, defaults to `"<s>"`):
          The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
  
          <Tip>
  
          When building a sequence using special tokens, this is not the token that is used for the beginning of
          sequence. The token used is the `cls_token`.
  
          </Tip>
  
      eos_token (`str`, *optional*, defaults to `"</s>"`):
          The end of sequence token.
  
          <Tip>
  
          When building a sequence using special tokens, this is not the token that is used for the end of sequence.
          The token used is the `sep_token`.
  
          </Tip>
  
      sep_token (`str`, *optional*, defaults to `"</s>"`):
          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.
      cls_token (`str`, *optional*, defaults to `"<s>"`):
          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.
      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.
      pad_token (`str`, *optional*, defaults to `"<pad>"`):
          The token used for padding, for example when batching sequences of different lengths.
      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.
      add_prefix_space (`bool`, *optional*, defaults to `False`):
          Whether or not to add an initial space to the input. This allows to treat the leading word just as any
          other word. (BART tokenizer detect beginning of words by the preceding space).
      max_cell_length (`int`, *optional*, defaults to 15):
          Maximum number of characters per cell when linearizing a table. If this number is exceeded, truncation
          takes place.
docstr: <Tip warning={true}>
  
  This method is deprecated, `__call__` should be used instead.
  
  </Tip>
docstr_tok: ["<","Tip","warning=","{","true","}",">","This","method","is","deprecated",",","`","__call__","`","should","be","used","instead",".","<","\/Tip",">"]
code: def batch_encode_plus(
      self,
      table: Union["pd.DataFrame", List["pd.DataFrame"]],
      query: Optional[List[TextInput]] = None,
      answer: List[str] = None,
      add_special_tokens: bool = True,
      padding: Union[bool, str, PaddingStrategy] = False,
      truncation: Union[bool, str] = None,
      max_length: Optional[int] = None,
      pad_to_multiple_of: Optional[int] = None,
      return_tensors: Optional[Union[str, TensorType]] = None,
      return_token_type_ids: Optional[bool] = None,
      return_attention_mask: Optional[bool] = None,
      return_overflowing_tokens: bool = False,
      return_special_tokens_mask: bool = False,
      return_offsets_mapping: bool = False,
      return_length: bool = False,
      verbose: bool = True,
      **kwargs,
  ) -> BatchEncoding:
      """
      <Tip warning={true}>
  
      This method is deprecated, `__call__` should be used instead.
  
      </Tip>
      """
      # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
      (
          padding_strategy,
          truncation_strategy,
          max_length,
          kwargs,
      ) = self._get_padding_truncation_strategies(
          padding=padding,
          truncation=truncation,
          max_length=max_length,
          pad_to_multiple_of=pad_to_multiple_of,
          verbose=verbose,
          **kwargs,
      )
  
      return self._batch_encode_plus(
          table=table,
          query=query,
          answer=answer,
          add_special_tokens=add_special_tokens,
          padding_strategy=padding_strategy,
          truncation_strategy=truncation_strategy,
          max_length=max_length,
          pad_to_multiple_of=pad_to_multiple_of,
          return_tensors=return_tensors,
          return_token_type_ids=return_token_type_ids,
          return_attention_mask=return_attention_mask,
          return_overflowing_tokens=return_overflowing_tokens,
          return_special_tokens_mask=return_special_tokens_mask,
          return_offsets_mapping=return_offsets_mapping,
          return_length=return_length,
          verbose=verbose,
          **kwargs,
      )
  
imports: import json
  import os
  import random
  from functools import lru_cache
  from typing import Dict, List, Optional, Tuple, Union
  import regex
  from ...file_utils import ExplicitEnum, PaddingStrategy, TensorType, add_end_docstrings, is_pandas_available
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...tokenization_utils_base import ENCODE_KWARGS_DOCSTRING, BatchEncoding, TextInput, TruncationStrategy
  from ...utils import logging
label_desc: Use image node_id 16 for calling the TapexTokenizer obj's underlying member method code with example usage: obj.batch_encode_plus(table, query, answer, add_special_tokens, padding, truncation, max_length, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose) and returns: self