code: def test_call_pytorch(self):
      # Initialize image_processing
      image_processing = self.image_processing_class(
          **self.image_processor_dict
      )
      # 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 not batched input
      encoded_images = image_processing(
          image_inputs[0], return_tensors="pt"
      ).pixel_values
      expected_output_image_shape = (
          self.image_processor_tester.expected_output_image_shape(
              [image_inputs[0]]
          )
      )
      self.assertEqual(
          tuple(encoded_images.shape), (1, *expected_output_image_shape)
      )
  
imports: import unittest
  import numpy
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
label_desc: Use image node_id 8 for calling the Swin2SRImageProcessingTest obj's underlying member method code with example usage: obj.test_call_pytorch() without return types