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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def sentence_vectorizer(text: str, use_mean: bool = True):
 """ Get sentence vector from text If a word is not in the vocabulary, KeyError will be raised. :param string text: text input :param boolean use_mean: if `True` use mean of all word vectors else use summation :return: sentence vector of given input text """

    words = word_tokenize(text, engine="ulmfit")
    vec = np.zeros((1, WV_DIM))

    for word in words:
        if word == " ":
            word = "xxspace"
        elif word == "\n":
            word = "xxeol"

        if word in _MODEL.wv.index2word:
            vec += _MODEL.wv.word_vec(word)
        else:
            pass

    if use_mean:
        vec /= len(words)

    return vec