docstr: Test _crop_audio_label method with random frame offset.
  Given the ``num_frames`` argument, the method returns ``num_frames`` samples in the waveform
  starting with random offset, the corresponding labels, and the length of the cropped waveform.
  The length of the cropped waveform should be identical to ``num_frames``.
  The dimension of the labels should be identical to HuBERT transformer layer output frame dimension.
code: def test_rand_crop(self, num_frames):
      """Test _crop_audio_label method with random frame offset.
      Given the ``num_frames`` argument, the method returns ``num_frames`` samples in the waveform
      starting with random offset, the corresponding labels, and the length of the cropped waveform.
      The length of the cropped waveform should be identical to ``num_frames``.
      The dimension of the labels should be identical to HuBERT transformer layer output frame dimension.
      """
      sample_rate = 16000
      waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05)
      length = waveform.shape[1]
      label = torch.rand(50)
      model = hubert_base()
      waveform_out, label_out, length = _crop_audio_label(
          waveform, label, length, num_frames, rand_crop=False
      )
      hubert_feat = model.extract_features(
          waveform_out.unsqueeze(0), num_layers=1
      )[0][0]
      self.assertEqual(waveform_out.shape[0], num_frames, length)
      self.assertEqual(label_out.shape[0], hubert_feat.shape[1])
  
file_path: audio/test/torchaudio_unittest/example/hubert/test_crop_audio_label.py
imports: import torch
  from dataset.hubert_dataset import _crop_audio_label
  from parameterized import parameterized
  from torchaudio.models import hubert_base
  from torchaudio_unittest.common_utils import get_whitenoise, TorchaudioTestCase
label_desc: Use image node_id 3 for calling the TestCropAudioLabel obj's underlying member method code with example usage: obj.test_rand_crop(num_frames) without return types