def METHOD_NAME(self):
    model = conformer_wav2vec2_base()
    model.eval()
    batch_size, num_frames, in_features = 3, 1024, 64
    features = torch.randn(batch_size, num_frames, in_features)
    lengths = torch.randint(
        low=0,
        high=num_frames,
        size=[
            batch_size,
        ],
    )
    ref_out, ref_len = model(features, lengths)
    scripted = torch_script(model)
    hyp_out, hyp_len = scripted(features, lengths)
    self.assertEqual(hyp_out, ref_out)
    self.assertEqual(hyp_len, ref_len)