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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 count_n_grams_py_polarity(self, data_set_reader, n_grams, filters):
 """ Returns a map of n-gram and the number of times it appeared in positive context and the number of times it appeared in negative context in dataset file. :param data_set_reader: Dataset containing tweets and their classification :param n_grams: n-grams to count occurrences for :param filters: filters to apply to tweets in dataset before searching for n-grams :return: Map of Counter instances for n-grams in nGrams Collection """

        self.data_set_reader = data_set_reader
        token_trie = TokenTrie(n_grams)

        counter = {}

        # Todo: parallelize
        for entry in data_set_reader.items():
            tweet = filters.apply(entry.get_tweet())
            tokens = token_trie.find_optimal_tokenization(RegexFilters.WHITESPACE.split(tweet))

            for n_gram in tokens:
                n_gram_words = RegexFilters.WHITESPACE.split(n_gram)
                if self.contains_illegal_word(n_gram_words):
                    continue
                if not n_gram in counter:
                    counter[n_gram] = self.Counter()

                if entry.get_classification().is_positive():
                    counter[n_gram].num_positive += 1
                elif entry.get_classification().is_negative():
                    counter[n_gram].num_negative += 1

        return counter