code: def test_processor_case_2(self):
      # case 2: document image classification (training, inference) + token classification (inference), apply_ocr=False
  
      feature_extractor = LayoutLMv2FeatureExtractor(apply_ocr=False)
      tokenizers = self.get_tokenizers
      images = self.get_images
  
      for tokenizer in tokenizers:
          processor = LayoutLMv2Processor(
              feature_extractor=feature_extractor, tokenizer=tokenizer
          )
  
          # not batched
          words = ["hello", "world"]
          boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]
          input_processor = processor(
              images[0], words, boxes=boxes, return_tensors="pt"
          )
  
          # verify keys
          expected_keys = [
              "input_ids",
              "bbox",
              "token_type_ids",
              "attention_mask",
              "image",
          ]
          actual_keys = list(input_processor.keys())
          for key in expected_keys:
              self.assertIn(key, actual_keys)
  
          # verify input_ids
          expected_decoding = "[CLS] hello world [SEP]"
          decoding = processor.decode(
              input_processor.input_ids.squeeze().tolist()
          )
          self.assertSequenceEqual(decoding, expected_decoding)
  
          # batched
          words = [["hello", "world"], ["my", "name", "is", "niels"]]
          boxes = [
              [[1, 2, 3, 4], [5, 6, 7, 8]],
              [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]],
          ]
          input_processor = processor(
              images,
              words,
              boxes=boxes,
              padding=True,
              return_tensors="pt",
          )
  
          # verify keys
          expected_keys = [
              "attention_mask",
              "bbox",
              "image",
              "input_ids",
              "token_type_ids",
          ]
          actual_keys = sorted(list(input_processor.keys()))
          self.assertListEqual(actual_keys, expected_keys)
  
          # verify input_ids
          expected_decoding = (
              "[CLS] hello world [SEP] [PAD] [PAD] [PAD]"
          )
          decoding = processor.decode(
              input_processor.input_ids[0].tolist()
          )
          self.assertSequenceEqual(decoding, expected_decoding)
  
          # verify bbox
          expected_bbox = [
              [0, 0, 0, 0],
              [3, 2, 5, 1],
              [6, 7, 4, 2],
              [3, 9, 2, 4],
              [1, 1, 2, 3],
              [1, 1, 2, 3],
              [1000, 1000, 1000, 1000],
          ]
          self.assertListEqual(
              input_processor.bbox[1].tolist(), expected_bbox
          )
  
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 4 for calling the LayoutLMv2ProcessorIntegrationTests obj's underlying member method code with example usage: obj.test_processor_case_2() without return types