code: def test_default_classification_and_regression(self):
      data_collator = default_data_collator
  
      features = [
          {"input_ids": [0, 1, 2, 3, 4], "label": i} for i in range(4)
      ]
      batch = data_collator(features, return_tensors="tf")
      self.assertEqual(batch["labels"].dtype, tf.int64)
  
      features = [
          {"input_ids": [0, 1, 2, 3, 4], "label": float(i)}
          for i in range(4)
      ]
      batch = data_collator(features, return_tensors="tf")
      self.assertEqual(batch["labels"].dtype, tf.float32)
  
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 5 for calling the TFDataCollatorIntegrationTest obj's underlying member method code with example usage: obj.test_default_classification_and_regression() without return types