code: def test_inference_no_head_absolute_embedding(self):
      model = BertGenerationDecoder.from_pretrained(
          "google/bert_for_seq_generation_L-24_bbc_encoder"
      )
      input_ids = torch.tensor(
          [[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]
      )
      with torch.no_grad():
          output = model(input_ids)[0]
      expected_shape = torch.Size([1, 8, 50358])
      self.assertEqual(output.shape, expected_shape)
      expected_slice = torch.tensor(
          [
              [
                  [-0.5788, -2.5994, -3.7054],
                  [0.0438, 4.7997, 1.8795],
                  [1.5862, 6.6409, 4.4638],
              ]
          ]
      )
      self.assertTrue(
          torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)
      )
  
imports: import unittest
  from transformers import BertGenerationConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the BertGenerationDecoderIntegrationTest obj's underlying member method code with example usage: obj.test_inference_no_head_absolute_embedding() without return types