code: def test_nsp(self):
      tokenizer = BertTokenizer(self.vocab_file)
      features = [
          {
              "input_ids": [0, 1, 2, 3, 4],
              "token_type_ids": [0, 1, 2, 3, 4],
              "next_sentence_label": i,
          }
          for i in range(2)
      ]
      data_collator = DataCollatorForLanguageModeling(
          tokenizer, return_tensors="tf"
      )
      batch = data_collator(features)
  
      self.assertEqual(batch["input_ids"].shape.as_list(), [2, 5])
      self.assertEqual(batch["token_type_ids"].shape.as_list(), [2, 5])
      self.assertEqual(batch["labels"].shape.as_list(), [2, 5])
      self.assertEqual(
          batch["next_sentence_label"].shape.as_list(), [2]
      )
  
      data_collator = DataCollatorForLanguageModeling(
          tokenizer, pad_to_multiple_of=8, return_tensors="tf"
      )
      batch = data_collator(features)
  
      self.assertEqual(batch["input_ids"].shape.as_list(), [2, 8])
      self.assertEqual(batch["token_type_ids"].shape.as_list(), [2, 8])
      self.assertEqual(batch["labels"].shape.as_list(), [2, 8])
      self.assertEqual(
          batch["next_sentence_label"].shape.as_list(), [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 13 for calling the TFDataCollatorIntegrationTest obj's underlying member method code with example usage: obj.test_nsp() without return types