code: def test_inference_no_head(self):
      model = ConvBertModel.from_pretrained("YituTech/conv-bert-base")
      input_ids = torch.tensor([[1, 2, 3, 4, 5, 6]])
      with torch.no_grad():
          output = model(input_ids)[0]
  
      expected_shape = torch.Size((1, 6, 768))
      self.assertEqual(output.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [
                  [-0.0864, -0.4898, -0.3677],
                  [0.1434, -0.2952, -0.7640],
                  [-0.0112, -0.4432, -0.5432],
              ]
          ]
      )
  
      self.assertTrue(
          torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)
      )
  
imports: import os
  import tempfile
  import unittest
  from transformers import ConvBertConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 1 for calling the ConvBertModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_no_head() without return types