code: def test_data_collator_with_padding(self):
      tokenizer = BertTokenizer(self.vocab_file)
      features = [
          {"input_ids": [0, 1, 2]},
          {"input_ids": [0, 1, 2, 3, 4, 5]},
      ]
  
      data_collator = DataCollatorWithPadding(
          tokenizer, return_tensors="np"
      )
      batch = data_collator(features)
      self.assertEqual(batch["input_ids"].shape, (2, 6))
      self.assertEqual(
          batch["input_ids"][0].tolist(),
          [0, 1, 2] + [tokenizer.pad_token_id] * 3,
      )
  
      data_collator = DataCollatorWithPadding(
          tokenizer,
          padding="max_length",
          max_length=10,
          return_tensors="np",
      )
      batch = data_collator(features)
      self.assertEqual(batch["input_ids"].shape, (2, 10))
  
      data_collator = DataCollatorWithPadding(
          tokenizer, pad_to_multiple_of=8, return_tensors="np"
      )
      batch = data_collator(features)
      self.assertEqual(batch["input_ids"].shape, (2, 8))
  
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 6 for calling the NumpyDataCollatorIntegrationTest obj's underlying member method code with example usage: obj.test_data_collator_with_padding() without return types