code: def test_equivalence_pad_and_create_pixel_mask(self):
      # Initialize feature_extractors
      feature_extractor_1 = self.feature_extraction_class(
          **self.feat_extract_dict
      )
      feature_extractor_2 = self.feature_extraction_class(
          do_resize=False, do_normalize=False
      )
      # create random PyTorch tensors
      image_inputs = prepare_image_inputs(
          self.feature_extract_tester,
          equal_resolution=False,
          torchify=True,
      )
      for image in image_inputs:
          self.assertIsInstance(image, torch.Tensor)
  
      # Test whether the method "pad_and_return_pixel_mask" and calling the feature extractor return the same tensors
      encoded_images_with_method = (
          feature_extractor_1.pad_and_create_pixel_mask(
              image_inputs, return_tensors="pt"
          )
      )
      encoded_images = feature_extractor_2(
          image_inputs, return_tensors="pt"
      )
  
      self.assertTrue(
          torch.allclose(
              encoded_images_with_method["pixel_values"],
              encoded_images["pixel_values"],
              atol=1e-4,
          )
      )
      self.assertTrue(
          torch.allclose(
              encoded_images_with_method["pixel_mask"],
              encoded_images["pixel_mask"],
              atol=1e-4,
          )
      )
  
imports: import json
  import pathlib
  import unittest
  import numpy
  from transformers.testing_utils import require_torch, require_vision, slow
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
label_desc: Use image node_id 8 for calling the DeformableDetrFeatureExtractionTest obj's underlying member method code with example usage: obj.test_equivalence_pad_and_create_pixel_mask() without return types