code: def test_inference_image_classification_head(self):
      model = Swinv2ForImageClassification.from_pretrained(
          "microsoft/swinv2-tiny-patch4-window8-256"
      ).to(torch_device)
      feature_extractor = self.default_feature_extractor
  
      image = Image.open(
          "./tests/fixtures/tests_samples/COCO/000000039769.png"
      )
      inputs = feature_extractor(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, 1000))
      self.assertEqual(outputs.logits.shape, expected_shape)
      expected_slice = torch.tensor([-0.3947, -0.4306, 0.0026]).to(
          torch_device
      )
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, :3], expected_slice, atol=1e-4
          )
      )
  
imports: import collections
  import inspect
  import unittest
  from transformers import Swinv2Config
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, 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
label_desc: Use image node_id 2 for calling the Swinv2ModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_image_classification_head() without return types