code: def test_data_collator_for_token_classification_works_with_pt_tensors(
      self,
  ):
      tokenizer = BertTokenizer(self.vocab_file)
      features = [
          {
              "input_ids": torch.tensor([0, 1, 2]),
              "labels": torch.tensor([0, 1, 2]),
          },
          {
              "input_ids": torch.tensor([0, 1, 2, 3, 4, 5]),
              "labels": torch.tensor([0, 1, 2, 3, 4, 5]),
          },
      ]
  
      data_collator = DataCollatorForTokenClassification(tokenizer)
      batch = data_collator(features)
      self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
      self.assertEqual(
          batch["input_ids"][0].tolist(),
          [0, 1, 2] + [tokenizer.pad_token_id] * 3,
      )
      self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
      self.assertEqual(
          batch["labels"][0].tolist(), [0, 1, 2] + [-100] * 3
      )
  
      data_collator = DataCollatorForTokenClassification(
          tokenizer, padding="max_length", max_length=10
      )
      batch = data_collator(features)
      self.assertEqual(batch["input_ids"].shape, torch.Size([2, 10]))
      self.assertEqual(batch["labels"].shape, torch.Size([2, 10]))
  
      data_collator = DataCollatorForTokenClassification(
          tokenizer, pad_to_multiple_of=8
      )
      batch = data_collator(features)
      self.assertEqual(batch["input_ids"].shape, torch.Size([2, 8]))
      self.assertEqual(batch["labels"].shape, torch.Size([2, 8]))
  
      data_collator = DataCollatorForTokenClassification(
          tokenizer, label_pad_token_id=-1
      )
      batch = data_collator(features)
      self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
      self.assertEqual(
          batch["input_ids"][0].tolist(),
          [0, 1, 2] + [tokenizer.pad_token_id] * 3,
      )
      self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
      self.assertEqual(
          batch["labels"][0].tolist(), [0, 1, 2] + [-1] * 3
      )
  
      for feature in features:
          feature.pop("labels")
  
      batch = data_collator(features)
      self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
      self.assertEqual(
          batch["input_ids"][0].tolist(),
          [0, 1, 2] + [tokenizer.pad_token_id] * 3,
      )
  
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_data_collator_for_token_classification_works_with_pt_tensors() without return types