code: def test_equivalence_padding(self):
      # Initialize image_processings
      image_processing_1 = self.image_processing_class(
          **self.image_processor_dict
      )
      image_processing_2 = self.image_processing_class(
          do_resize=False, do_normalize=False, do_rescale=False
      )
      # create random PyTorch tensors
      image_inputs = self.image_processor_tester.prepare_image_inputs(
          equal_resolution=False, torchify=True
      )
      for image in image_inputs:
          self.assertIsInstance(image, torch.Tensor)
  
      # Test whether the method "pad" and calling the image processor return the same tensors
      encoded_images_with_method = image_processing_1.pad(
          image_inputs, return_tensors="pt"
      )
      encoded_images = image_processing_2(
          image_inputs, return_tensors="pt"
      )
  
      self.assertTrue(
          torch.allclose(
              encoded_images_with_method["pixel_values"],
              encoded_images["pixel_values"],
              atol=1e-4,
          )
      )
  
imports: import json
  import pathlib
  import unittest
  from transformers.testing_utils import require_torch, require_vision, slow
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_image_processing_common import AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs
label_desc: Use image node_id 5 for calling the YolosImageProcessingTest obj's underlying member method code with example usage: obj.test_equivalence_padding() without return types