code: def test_inference_image_classification_head(self):
      image_processor = PoolFormerImageProcessor()
      model = PoolFormerForImageClassification.from_pretrained(
          "sail/poolformer_s12"
      ).to(torch_device)
  
      inputs = image_processor(
          images=prepare_img(), 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([-0.6113, 0.1685, -0.0492]).to(
          torch_device
      )
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, :3], expected_slice, atol=1e-4
          )
      )
  
imports: import unittest
  from transformers import is_torch_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the PoolFormerModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_image_classification_head() without return types