code: def test_inference_with_head(self):
      model = TvpForVideoGrounding.from_pretrained(
          "Jiqing/tiny-random-tvp"
      ).to(torch_device)
  
      image_processor = self.default_image_processor
      image = prepare_img()
      encoding = image_processor(images=image, return_tensors="pt")
      input_ids = torch.tensor([[1, 2]])
      attention_mask = torch.tensor([[1, 1]])
      encoding.update(
          {"input_ids": input_ids, "attention_mask": attention_mask}
      )
      encoding.to(torch_device)
  
      with torch.no_grad():
          outputs = model(**encoding)
  
      expected_shape = torch.Size((1, 2))
      assert outputs.logits.shape == expected_shape
      expected_slice = torch.tensor([[0.5061, 0.4988]]).to(torch_device)
      self.assertTrue(
          torch.allclose(outputs.logits, expected_slice, atol=1e-4)
      )
  
imports: import unittest
  from transformers import ResNetConfig, TvpConfig
  from transformers.testing_utils import require_torch, require_vision, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the TvpModelIntegrationTests obj's underlying member method code with example usage: obj.test_inference_with_head() without return types