code: def _feature_extractor_test(self, model):
      batch_size, num_frames = 3, 1024
  
      model = model.wav2vec2
      model.eval()
      num_layers = len(model.encoder.transformer.layers)
  
      waveforms = torch.randn(batch_size, num_frames)
      lengths = torch.randint(
          low=0,
          high=num_frames,
          size=[
              batch_size,
          ],
      )
  
      # Not providing num_layers returns all the intermediate features from
      # tranformer layers
      all_features, lengths_ = model.extract_features(
          waveforms, lengths, num_layers=None
      )
      assert len(all_features) == num_layers
      for features in all_features:
          assert features.ndim == 3
          assert features.shape[0] == batch_size
      assert lengths_.shape == torch.Size([batch_size])
  
      # Limiting the number of layers to `l`.
      for l in range(1, num_layers + 1):
          features, lengths_ = model.extract_features(
              waveforms, lengths, num_layers=l
          )
          assert len(features) == l
          for i in range(l):
              self.assertEqual(all_features[i], features[i])
          assert lengths_.shape == torch.Size([batch_size])
  
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 4 for calling the TestHuBERTPretrainModel obj's underlying member method code with example usage: obj._feature_extractor_test(model) without return types