code: def _test_no_pad_and_pad(self, no_pad_features, pad_features):
      tokenizer = BertTokenizer(self.vocab_file)
      data_collator = DataCollatorForLanguageModeling(
          tokenizer, mlm=False
      )
      batch = data_collator(no_pad_features)
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
      self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
  
      batch = data_collator(pad_features)
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
      self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
  
      data_collator = DataCollatorForLanguageModeling(
          tokenizer, mlm=False, pad_to_multiple_of=8
      )
      batch = data_collator(no_pad_features)
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
      self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
  
      batch = data_collator(pad_features)
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
      self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
  
      tokenizer._pad_token = None
      data_collator = DataCollatorForLanguageModeling(
          tokenizer, mlm=False
      )
      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)
      batch = data_collator(no_pad_features)
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
      self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
  
      masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
      self.assertTrue(torch.any(masked_tokens))
      self.assertTrue(
          all(
              x == -100
              for x in batch["labels"][~masked_tokens].tolist()
          )
      )
  
      batch = data_collator(pad_features)
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
      self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
  
      masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
      self.assertTrue(torch.any(masked_tokens))
      self.assertTrue(
          all(
              x == -100
              for x in batch["labels"][~masked_tokens].tolist()
          )
      )
  
      data_collator = DataCollatorForLanguageModeling(
          tokenizer, pad_to_multiple_of=8
      )
      batch = data_collator(no_pad_features)
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
      self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
  
      masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
      self.assertTrue(torch.any(masked_tokens))
      self.assertTrue(
          all(
              x == -100
              for x in batch["labels"][~masked_tokens].tolist()
          )
      )
  
      batch = data_collator(pad_features)
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
      self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
  
      masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
      self.assertTrue(torch.any(masked_tokens))
      self.assertTrue(
          all(
              x == -100
              for x in batch["labels"][~masked_tokens].tolist()
          )
      )
  
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 8 for calling the DataCollatorIntegrationTest obj's underlying member method code with example usage: obj._test_no_pad_and_pad(no_pad_features, pad_features) without return types