code: def test_data_collator_for_whole_word_mask(self):
      tokenizer = BertTokenizer(self.vocab_file)
      data_collator = DataCollatorForWholeWordMask(
          tokenizer, return_tensors="tf"
      )
  
      features = [
          {"input_ids": list(range(10))},
          {"input_ids": list(range(10))},
      ]
      batch = data_collator(features)
      self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
      self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
  
      # Features can already be tensors
      features = [
          {"input_ids": np.arange(10)},
          {"input_ids": np.arange(10)},
      ]
      batch = data_collator(features)
      self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
      self.assertEqual(batch["labels"].shape.as_list(), [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 11 for calling the TFDataCollatorIntegrationTest obj's underlying member method code with example usage: obj.test_data_collator_for_whole_word_mask() without return types