code: def test_plm(self):
      tokenizer = BertTokenizer(self.vocab_file)
      no_pad_features = [
          {"input_ids": list(range(10))},
          {"input_ids": list(range(10))},
      ]
      pad_features = [
          {"input_ids": list(range(5))},
          {"input_ids": list(range(10))},
      ]
  
      data_collator = DataCollatorForPermutationLanguageModeling(
          tokenizer
      )
  
      batch = data_collator(pad_features)
      self.assertIsInstance(batch, dict)
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
      self.assertEqual(
          batch["perm_mask"].shape, torch.Size((2, 10, 10))
      )
      self.assertEqual(
          batch["target_mapping"].shape, torch.Size((2, 10, 10))
      )
      self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
  
      batch = data_collator(no_pad_features)
      self.assertIsInstance(batch, dict)
      self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
      self.assertEqual(
          batch["perm_mask"].shape, torch.Size((2, 10, 10))
      )
      self.assertEqual(
          batch["target_mapping"].shape, torch.Size((2, 10, 10))
      )
      self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
  
      example = [np.random.randint(0, 5, [5])]
      with self.assertRaises(ValueError):
          # Expect error due to odd sequence length
          data_collator(example)
  
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 DataCollatorIntegrationTest obj's underlying member method code with example usage: obj.test_plm() without return types