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.
class_docstr_tok: ["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","."]
code: def _batch_encode_plus(
      self,
      batch_text_or_text_pairs: Union[
          List[TextInput],
          List[TextInputPair],
          List[PreTokenizedInput],
      ],
      is_pair: bool = None,
      boxes: Optional[List[List[List[int]]]] = None,
      word_labels: Optional[List[List[int]]] = None,
      add_special_tokens: bool = True,
      padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
      truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
      max_length: Optional[int] = None,
      stride: int = 0,
      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:
      if return_offsets_mapping:
          raise NotImplementedError(
              "return_offset_mapping is not available when using Python tokenizers. "
              "To use this feature, change your tokenizer to one deriving from "
              "transformers.PreTrainedTokenizerFast."
          )
  
      batch_outputs = self._batch_prepare_for_model(
          batch_text_or_text_pairs=batch_text_or_text_pairs,
          is_pair=is_pair,
          boxes=boxes,
          word_labels=word_labels,
          add_special_tokens=add_special_tokens,
          padding_strategy=padding_strategy,
          truncation_strategy=truncation_strategy,
          max_length=max_length,
          stride=stride,
          pad_to_multiple_of=pad_to_multiple_of,
          return_attention_mask=return_attention_mask,
          return_token_type_ids=return_token_type_ids,
          return_overflowing_tokens=return_overflowing_tokens,
          return_special_tokens_mask=return_special_tokens_mask,
          return_length=return_length,
          return_tensors=return_tensors,
          verbose=verbose,
      )
  
      return BatchEncoding(batch_outputs)
  
imports: import collections
  import os
  import sys
  import unicodedata
  from typing import Dict, List, Optional, Tuple, Union
  from ...tokenization_utils import 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 15 for calling the LayoutLMv2Tokenizer obj's underlying member method code with example usage: obj._batch_encode_plus(batch_text_or_text_pairs, is_pair, boxes, word_labels, add_special_tokens, padding_strategy, truncation_strategy, max_length, stride, 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: BatchEncoding