def METHOD_NAME(self, in_path=None, w2v=None, overwrite=False, data_iter=None, workers=14, epochs=2, min_count=10, window=10, vector_size=300,
                unigram_table_size=100000000):
    """Add vectors to an existing vocabulary and save changes to the vocab_path.
    Args:
        in_path (str):
            Path to the data.txt that was created by the MakeVocab.make() function.
        w2v (Word2Vec, optional):
            An existing word2vec instance. Default: None
        overwrite (bool):
            If True it will overwrite existing vectors in the vocabulary. Default: False
        data_iter (iterator):
            If you want to provide a customer iterator over the data use this. If yes, then in_path is not needed.
        **: Word2Vec arguments
    Returns:
        A trained word2vec model.
    """
    if w2v is None:
        if data_iter is None:
            data = SimpleIter(in_path)
        else:
            data = data_iter
        w2v = Word2Vec(data, window=window, min_count=min_count, workers=workers, vector_size=vector_size, epochs=epochs)
    for word in w2v.wv.key_to_index.keys():
        if word in self.vocab:
            if overwrite:
                self.vocab.add_vec(word, w2v.wv.get_vector(word))
            else:
                if self.vocab.vec(word) is None:
                    self.vocab.add_vec(word, w2v.wv.get_vector(word))
    # Save the vocab again, now with vectors
    self.vocab.make_unigram_table(table_size=unigram_table_size)
    self.vocab.save(path=self.vocab_path)
    return w2v