code: def test_data_collator_for_whole_word_mask(self):
      features = [
          {"input_ids": list(range(10))},
          {"input_ids": list(range(10))},
      ]
  
      tokenizer = BertTokenizer(self.vocab_file)
      data_collator = DataCollatorForWholeWordMask(
          tokenizer, return_tensors="pt"
      )
      batch = data_collator(features)
  
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
      self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
  
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 10 for calling the DataCollatorIntegrationTest obj's underlying member method code with example usage: obj.test_data_collator_for_whole_word_mask() without return types