def METHOD_NAME(data_pipeline, samples: List):
    mel_features = [_spectrogram_transform(sample[0].squeeze()).transpose(1, 0) for sample in samples]
    features = torch.nn.utils.rnn.pad_sequence(mel_features, batch_first=True)
    features = data_pipeline(features)
    lengths = torch.tensor([elem.shape[0] for elem in mel_features], dtype=torch.int32)
    return features, lengths