code: def test_inference_model(self):
      model = (
          PvtModel.from_pretrained("Zetatech/pvt-tiny-224")
          .to(torch_device)
          .eval()
      )
  
      image_processor = PvtImageProcessor.from_pretrained(
          "Zetatech/pvt-tiny-224"
      )
      image = prepare_img()
      inputs = image_processor(images=image, return_tensors="pt")
      pixel_values = inputs.pixel_values.to(torch_device)
  
      # forward pass
      with torch.no_grad():
          outputs = model(pixel_values)
  
      # verify the logits
      expected_shape = torch.Size((1, 50, 512))
      self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [-0.3086, 1.0402, 1.1816],
              [-0.2880, 0.5781, 0.6124],
              [0.1480, 0.6129, -0.0590],
          ]
      ).to(torch_device)
  
      self.assertTrue(
          torch.allclose(
              outputs.last_hidden_state[0, :3, :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_accelerate, require_torch, require_torch_accelerator, require_torch_fp16, 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 2 for calling the PvtModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_model() without return types