code: def _test_torchscript(self, model):
      model.eval()
  
      batch_size, num_frames = 3, 1024
      waveforms = torch.randn(batch_size, num_frames)
      # Compute results with original model
      ref_out, ref_len = model(waveforms)
      # Compute results with scripted model
      scripted = torch_script(model)
      hyp_out, hyp_len = scripted(waveforms)
  
      self.assertEqual(hyp_out, ref_out)
      self.assertEqual(hyp_len, ref_len)
  
imports: import math
  import os
  from typing import Tuple
  import torch
  import torch.nn.functional
  from parameterized import parameterized
  from torchaudio.models.wav2vec2 import hubert_base, hubert_large, hubert_pretrain_base, hubert_pretrain_large, hubert_pretrain_xlarge, hubert_xlarge, wav2vec2_base, wav2vec2_large, wav2vec2_large_lv60k, wavlm_base, wavlm_large
  from torchaudio_unittest.common_utils import skipIfNoCuda, skipIfNoQengine, torch_script, TorchaudioTestCase
label_desc: Use image node_id 7 for calling the TestWavLMModel obj's underlying member method code with example usage: obj._test_torchscript(model) without return types