def METHOD_NAME(self, sentences: list, model, unseen=None) -> np.ndarray:
    """Generate vectors for each sentence (list) in a list of sentences. Vector is simply a
    sum of vectors for individual words.
    Parameters
    ----------
    sentences : list, array
        List with sentences
    model : word2vec.Word2Vec
        Gensim word2vec model
    unseen : None, str
        Keyword for unseen words. If None, those words are skipped.
        https://stats.stackexchange.com/questions/163005/how-to-set-the-dictionary-for-text-analysis-using-neural-networks/163032#163032
    Returns
    -------
    np.array
    """
    keys = set(model.wv.key_to_index.keys())
    vec = []
    if unseen:
        unseen_vec = model.wv.get_vector(unseen)
    for sentence in sentences:
        if unseen:
            vec.append(
                sum([
                    model.wv.get_vector(y) if y in set(sentence) &
                    keys else unseen_vec for y in sentence
                ]))
        else:
            vec.append(
                sum([
                    model.wv.get_vector(y)
                    for y in sentence
                    if y in set(sentence) & keys
                ]))
    return np.array(vec)