def METHOD_NAME(
        self, input: torch.Tensor, input_lengths: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
    wavs = [wav[: input_lengths[i]] for i, wav in enumerate(input)]
    self.upstream.eval()
    with torch.no_grad():
        feats = self.upstream(wavs)
    feats = self.featurizer(wavs, feats)
    if self.args.tile_factor != 1:
        feats = self._tile_representations(feats)
    input_feats = pad_list(feats, 0.0)
    feats_lens = torch.tensor([f.shape[0] for f in feats], dtype=torch.long)
    # Saving CUDA Memory
    del feats
    return input_feats, feats_lens