code: def _test_no_pad_and_pad(self, no_pad_features, pad_features):
      tokenizer = BertTokenizer(self.vocab_file)
      data_collator = DataCollatorForLanguageModeling(
          tokenizer, mlm=False, return_tensors="tf"
      )
      batch = data_collator(no_pad_features)
      self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
      self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
  
      batch = data_collator(pad_features)
      self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
      self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
  
      data_collator = DataCollatorForLanguageModeling(
          tokenizer,
          mlm=False,
          pad_to_multiple_of=8,
          return_tensors="tf",
      )
      batch = data_collator(no_pad_features)
      self.assertEqual(batch["input_ids"].shape.as_list(), [2, 16])
      self.assertEqual(batch["labels"].shape.as_list(), [2, 16])
  
      batch = data_collator(pad_features)
      self.assertEqual(batch["input_ids"].shape.as_list(), [2, 16])
      self.assertEqual(batch["labels"].shape.as_list(), [2, 16])
  
      tokenizer._pad_token = None
      data_collator = DataCollatorForLanguageModeling(
          tokenizer, mlm=False, return_tensors="tf"
      )
      with self.assertRaises(ValueError):
          # Expect error due to padding token missing
          data_collator(pad_features)
  
      set_seed(42)  # For reproducibility
      tokenizer = BertTokenizer(self.vocab_file)
      data_collator = DataCollatorForLanguageModeling(
          tokenizer, return_tensors="tf"
      )
      batch = data_collator(no_pad_features)
      self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
      self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
  
      masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
      self.assertTrue(tf.reduce_any(masked_tokens))
      # self.assertTrue(all(x == -100 for x in batch["labels"].numpy()[~masked_tokens.numpy()].tolist()))
  
      batch = data_collator(pad_features, return_tensors="tf")
      self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
      self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
  
      masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
      self.assertTrue(tf.reduce_any(masked_tokens))
      # self.assertTrue(all(x == -100 for x in batch["labels"].numpy()[~masked_tokens.numpy()].tolist()))
  
      data_collator = DataCollatorForLanguageModeling(
          tokenizer, pad_to_multiple_of=8, return_tensors="tf"
      )
      batch = data_collator(no_pad_features)
      self.assertEqual(batch["input_ids"].shape.as_list(), [2, 16])
      self.assertEqual(batch["labels"].shape.as_list(), [2, 16])
  
      masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
      self.assertTrue(tf.reduce_any(masked_tokens))
      # self.assertTrue(all(x == -100 for x in batch["labels"].numpy()[~masked_tokens.numpy()].tolist()))
  
      batch = data_collator(pad_features, return_tensors="tf")
      self.assertEqual(batch["input_ids"].shape.as_list(), [2, 16])
      self.assertEqual(batch["labels"].shape.as_list(), [2, 16])
  
      masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
      self.assertTrue(tf.reduce_any(masked_tokens))
  
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 9 for calling the TFDataCollatorIntegrationTest obj's underlying member method code with example usage: obj._test_no_pad_and_pad(no_pad_features, pad_features) without return types