code: def test_overflowing_tokens(self):
      # In the case of overflowing tokens, test that we still have 1-to-1 mapping between the images and input_ids (sequences that are too long are broken down into multiple sequences).
  
      from datasets import load_dataset
  
      # set up
      datasets = load_dataset("nielsr/funsd")
      processor = LayoutLMv2Processor.from_pretrained(
          "microsoft/layoutlmv2-base-uncased", revision="no_ocr"
      )
  
      def preprocess_data(examples):
          images = [
              Image.open(path).convert("RGB")
              for path in examples["image_path"]
          ]
          words = examples["words"]
          boxes = examples["bboxes"]
          word_labels = examples["ner_tags"]
          encoded_inputs = processor(
              images,
              words,
              boxes=boxes,
              word_labels=word_labels,
              padding="max_length",
              truncation=True,
              return_overflowing_tokens=True,
              stride=50,
              return_offsets_mapping=True,
              return_tensors="pt",
          )
          return encoded_inputs
  
      train_data = preprocess_data(datasets["train"])
  
      self.assertEqual(
          len(train_data["image"]), len(train_data["input_ids"])
      )
  
imports: import json
  import os
  import shutil
  import tempfile
  import unittest
  from typing import List
  from transformers import PreTrainedTokenizer, PreTrainedTokenizerBase, PreTrainedTokenizerFast
  from transformers.models.layoutlmv2 import LayoutLMv2Tokenizer, LayoutLMv2TokenizerFast
  from transformers.models.layoutlmv2.tokenization_layoutlmv2 import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_pytesseract, require_tokenizers, require_torch, slow
  from transformers.utils import FEATURE_EXTRACTOR_NAME, cached_property, is_pytesseract_available
label_desc: Use image node_id 9 for calling the LayoutLMv2ProcessorTest obj's underlying member method code with example usage: obj.test_overflowing_tokens() and returns: encoded_inputs