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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 user_twitter_list_bag_of_words(twitter_list_corpus, sent_tokenize, _treebank_word_tokenize, tagger, lemmatizer, lemmatize, stopset, first_cap_re, all_cap_re, digits_punctuation_whitespace_re, pos_set):
 """ Extract a bag-of-words for a corpus of Twitter lists pertaining to a Twitter user. Inputs: - twitter_list_corpus: A python list of Twitter lists in json format. - lemmatizing: A string containing one of the following: "porter", "snowball" or "wordnet". Output: - bag_of_words: A bag-of-words in python dictionary format. - lemma_to_keywordbag_total: Aggregated python dictionary that maps stems/lemmas to original topic keywords. """

    # Extract a bag-of-words from a list of Twitter lists.
    # May result in empty sets
    list_of_keyword_sets, list_of_lemma_to_keywordbags = clean_list_of_twitter_list(twitter_list_corpus,
                                                                                    sent_tokenize, _treebank_word_tokenize,
                                                                                    tagger, lemmatizer, lemmatize, stopset,
                                                                                    first_cap_re, all_cap_re, digits_punctuation_whitespace_re,
                                                                                    pos_set)

    # Reduce keyword sets.
    bag_of_words = reduce_list_of_bags_of_words(list_of_keyword_sets)

    # Reduce lemma to keywordbag maps.
    lemma_to_keywordbag_total = defaultdict(lambda: defaultdict(int))
    for lemma_to_keywordbag in list_of_lemma_to_keywordbags:
        for lemma, keywordbag in lemma_to_keywordbag.items():
            for keyword, multiplicity in keywordbag.items():
                lemma_to_keywordbag_total[lemma][keyword] += multiplicity

    return bag_of_words, lemma_to_keywordbag_total