code: def test_inference_diarization(self):
      model = WavLMForAudioFrameClassification.from_pretrained(
          "microsoft/wavlm-base-plus-sd"
      ).to(torch_device)
      processor = Wav2Vec2FeatureExtractor.from_pretrained(
          "microsoft/wavlm-base-plus-sd"
      )
      input_data = self._load_superb("sd", 4)
      inputs = processor(
          input_data["speech"],
          return_tensors="pt",
          padding=True,
          sampling_rate=16_000,
      )
  
      input_values = inputs.input_values.to(torch_device)
      attention_mask = inputs.attention_mask.to(torch_device)
      with torch.no_grad():
          outputs = model(input_values, attention_mask=attention_mask)
      # labels is a one-hot array of shape (num_frames, num_speakers)
      labels = (outputs.logits > 0).long()
  
      # s3prl logits for the same batch
      expected_logits = torch.tensor(
          [
              [
                  [-5.9566, -8.6554],
                  [-5.7137, -8.9386],
                  [-5.7906, -7.0973],
                  [-5.7829, -5.9999],
              ],
              [
                  [-5.2086, -7.7878],
                  [-4.8890, -7.9312],
                  [-4.2004, -3.9101],
                  [-5.4480, -4.6932],
              ],
              [
                  [-4.6105, -6.7178],
                  [-5.1930, -6.1635],
                  [-2.6228, -4.1123],
                  [-2.7646, -3.1576],
              ],
              [
                  [-4.4477, -7.9206],
                  [-3.9339, -7.3707],
                  [-4.9528, -4.8242],
                  [-3.6921, -2.9687],
              ],
          ],
          device=torch_device,
      )
      self.assertEqual(labels[0, :, 0].sum(), 258)
      self.assertEqual(labels[0, :, 1].sum(), 647)
      # TODO: update the tolerance after the CI moves to torch 1.10
      self.assertTrue(
          torch.allclose(
              outputs.logits[:, :4], expected_logits, atol=1e-2
          )
      )
  
imports: import math
  import unittest
  import pytest
  from datasets import load_dataset
  from transformers import WavLMConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 5 for calling the WavLMModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_diarization() without return types