code: def test_inference_large(self):
      model = WavLMModel.from_pretrained("microsoft/wavlm-large").to(
          torch_device
      )
      feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
          "microsoft/wavlm-large", return_attention_mask=True
      )
  
      input_speech = self._load_datasamples(2)
  
      inputs = feature_extractor(
          input_speech, return_tensors="pt", padding=True
      )
  
      input_values = inputs.input_values.to(torch_device)
      attention_mask = inputs.attention_mask.to(torch_device)
  
      with torch.no_grad():
          hidden_states_slice = (
              model(input_values, attention_mask=attention_mask)
              .last_hidden_state[:, -2:, -2:]
              .cpu()
          )
  
      EXPECTED_HIDDEN_STATES_SLICE = torch.tensor(
          [
              [[0.2122, 0.0500], [0.2118, 0.0563]],
              [[0.1353, 0.1818], [0.2453, 0.0595]],
          ]
      )
  
      self.assertTrue(
          torch.allclose(
              hidden_states_slice,
              EXPECTED_HIDDEN_STATES_SLICE,
              rtol=5e-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 4 for calling the WavLMModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_large() without return types