def METHOD_NAME(self, model, feature_dim, aux_num_out):
    if aux_num_out is not None:
        model = model(aux_num_out=aux_num_out)
    else:
        model = model()
    model.eval()
    batch_size, num_frames = 3, 1024
    features = torch.randn(batch_size, num_frames, feature_dim)
    input_lengths = torch.zeros(batch_size)
    _, output_lengths = model(features, input_lengths)
    self.assertEqual(torch.zeros_like(output_lengths), output_lengths)
    _, output_lengths = model.extract_features(features, input_lengths)
    self.assertEqual(torch.zeros_like(output_lengths), output_lengths)