code: def _test_quantize_smoke_test(self, model):
      model.eval()
      batch_size, num_frames = 3, 1024
  
      # Remove the weight normalization forward hook
      model.encoder.transformer.pos_conv_embed.__prepare_scriptable__()
      quantized = tq.quantize_dynamic(
          model, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8
      )
  
      # A lazy way to check that Modules are different
      assert str(quantized) != str(
          model
      ), "Dynamic quantization did not modify the module."
  
      waveforms = torch.randn(batch_size, num_frames)
      _, _ = quantized(waveforms)
  
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 10 for calling the TestWavLMModel obj's underlying member method code with example usage: obj._test_quantize_smoke_test(model) without return types