code: def test_inference_no_head_absolute_embedding(self):
      model = LxmertModel.from_pretrained(
          LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST[0]
      )
      input_ids = torch.tensor(
          [[101, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 102]]
      )
      num_visual_features = 10
      _, visual_feats = np.random.seed(0), np.random.rand(
          1, num_visual_features, model.config.visual_feat_dim
      )
      _, visual_pos = np.random.seed(0), np.random.rand(
          1, num_visual_features, 4
      )
      visual_feats = torch.as_tensor(visual_feats, dtype=torch.float32)
      visual_pos = torch.as_tensor(visual_pos, dtype=torch.float32)
      output = model(
          input_ids, visual_feats=visual_feats, visual_pos=visual_pos
      )[0]
      expected_shape = torch.Size([1, 11, 768])
      self.assertEqual(expected_shape, output.shape)
      expected_slice = torch.tensor(
          [
              [
                  [0.2417, -0.9807, 0.1480],
                  [1.2541, -0.8320, 0.5112],
                  [1.4070, -1.1052, 0.6990],
              ]
          ]
      )
  
      self.assertTrue(
          torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)
      )
  
imports: import copy
  import unittest
  import numpy
  from transformers import LxmertConfig, is_tf_available, is_torch_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, ids_tensor
label_desc: Use image node_id 1 for calling the LxmertModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_no_head_absolute_embedding() without return types