code: def test_inference_no_head(self):
      model = TvpModel.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, 796, 128))
      assert outputs.last_hidden_state.shape == expected_shape
      expected_slice = torch.tensor(
          [
              [-0.4902, -0.4121, -1.7872],
              [-0.2184, 2.1211, -0.9371],
              [0.1180, 0.5003, -0.1727],
          ]
      ).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 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 2 for calling the TvpModelIntegrationTests obj's underlying member method code with example usage: obj.test_inference_no_head() without return types