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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 extract_user_keywords_generator(twitter_lists_gen, lemmatizing="wordnet"):
 """ Based on the user-related lists I have downloaded, annotate the users. Inputs: - twitter_lists_gen: A python generator that yields a user Twitter id and a generator of Twitter lists. - lemmatizing: A string containing one of the following: "porter", "snowball" or "wordnet". Yields: - user_twitter_id: A Twitter user id. - user_annotation: A python dictionary that contains two dicts: * bag_of_lemmas: Maps emmas to multiplicity. * lemma_to_keywordbag: A python dictionary that maps stems/lemmas to original topic keywords. """

    ####################################################################################################################
    # Extract keywords serially.
    ####################################################################################################################
    for user_twitter_id, twitter_lists_list in twitter_lists_gen:
        if twitter_lists_list is not None:
            if "lists" in twitter_lists_list.keys():
                twitter_lists_list = twitter_lists_list["lists"]

            bag_of_lemmas, lemma_to_keywordbag = user_twitter_list_bag_of_words(twitter_lists_list, lemmatizing)

            for lemma, keywordbag in lemma_to_keywordbag.items():
                lemma_to_keywordbag[lemma] = dict(keywordbag)
            lemma_to_keywordbag = dict(lemma_to_keywordbag)

            user_annotation = dict()
            user_annotation["bag_of_lemmas"] = bag_of_lemmas
            user_annotation["lemma_to_keywordbag"] = lemma_to_keywordbag

            yield user_twitter_id, user_annotation