code: def test_sop(self):
      tokenizer = BertTokenizer(self.vocab_file)
      features = [
          {
              "input_ids": torch.tensor([0, 1, 2, 3, 4]),
              "token_type_ids": torch.tensor([0, 1, 2, 3, 4]),
              "sentence_order_label": i,
          }
          for i in range(2)
      ]
      data_collator = DataCollatorForLanguageModeling(tokenizer)
      batch = data_collator(features)
  
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 5)))
      self.assertEqual(
          batch["token_type_ids"].shape, torch.Size((2, 5))
      )
      self.assertEqual(batch["labels"].shape, torch.Size((2, 5)))
      self.assertEqual(
          batch["sentence_order_label"].shape, torch.Size((2,))
      )
  
      data_collator = DataCollatorForLanguageModeling(
          tokenizer, pad_to_multiple_of=8
      )
      batch = data_collator(features)
  
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 8)))
      self.assertEqual(
          batch["token_type_ids"].shape, torch.Size((2, 8))
      )
      self.assertEqual(batch["labels"].shape, torch.Size((2, 8)))
      self.assertEqual(
          batch["sentence_order_label"].shape, torch.Size((2,))
      )
  
imports: import os
  import shutil
  import tempfile
  import unittest
  import numpy
  from transformers import BertTokenizer, DataCollatorForLanguageModeling, DataCollatorForPermutationLanguageModeling, DataCollatorForTokenClassification, DataCollatorForWholeWordMask, DataCollatorWithPadding, default_data_collator, is_tf_available, is_torch_available, set_seed
  from transformers.testing_utils import require_tf, require_torch
label_desc: Use image node_id 14 for calling the DataCollatorIntegrationTest obj's underlying member method code with example usage: obj.test_sop() without return types