code: def test_inference_image_super_resolution_head(self):
      processor = Swin2SRImageProcessor()
      model = Swin2SRForImageSuperResolution.from_pretrained(
          "caidas/swin2SR-classical-sr-x2-64"
      ).to(torch_device)
  
      image = Image.open(
          "./tests/fixtures/tests_samples/COCO/000000039769.png"
      )
      inputs = processor(images=image, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
  
      # verify the logits
      expected_shape = torch.Size([1, 3, 976, 1296])
      self.assertEqual(outputs.reconstruction.shape, expected_shape)
      expected_slice = torch.tensor(
          [
              [0.5458, 0.5546, 0.5638],
              [0.5526, 0.5565, 0.5651],
              [0.5396, 0.5426, 0.5621],
          ]
      ).to(torch_device)
      self.assertTrue(
          torch.allclose(
              outputs.reconstruction[0, 0, :3, :3],
              expected_slice,
              atol=1e-4,
          )
      )
  
imports: import unittest
  from transformers import Swin2SRConfig
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the Swin2SRModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_image_super_resolution_head() without return types