code: def test_inference_no_head_absolute_embedding(self):
      model = FlaubertModel.from_pretrained(
          "flaubert/flaubert_base_cased"
      )
      input_ids = torch.tensor(
          [[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]
      )
      with torch.no_grad():
          output = model(input_ids)[0]
      expected_shape = torch.Size((1, 11, 768))
      self.assertEqual(output.shape, expected_shape)
      expected_slice = torch.tensor(
          [
              [
                  [-2.6251, -1.4298, -0.0227],
                  [-2.8510, -1.6387, 0.2258],
                  [-2.8114, -1.1832, -0.3066],
              ]
          ]
      )
  
      self.assertTrue(
          torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)
      )
  
imports: import os
  import tempfile
  import unittest
  from transformers import FlaubertConfig, is_torch_available
  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, ids_tensor, random_attention_mask
label_desc: Use image node_id 1 for calling the FlaubertModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_no_head_absolute_embedding() without return types