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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 _handle_universal(self, X):
 """ Scan through the corpus to compute counts of each Universal Dependencies part-of-speech. Parameters X : list or generator Should be provided as a list of documents or a generator that yields a list of documents that contain a list of sentences that contain (token, tag) tuples. """

        jump = {
            # combine proper and regular nouns
            "NOUN": "noun", "PROPN": "noun",
            "ADJ": "adjective",
            "VERB": "verb",
            # include particles with adverbs
            "ADV": "adverb", "PART": "adverb",
            "ADP": "adposition",
            "PRON": "pronoun",
            "CCONJ": "conjunction",
            "PUNCT": "punctuation",
            "DET": "determiner",
            "NUM": "number",
            "INTJ": "interjection",
            "SYM": "symbol",
        }

        for tagged_doc in X:
            for tagged_sent in tagged_doc:
                for _, tag in tagged_sent:
                    if tag == "SPACE":
                        continue
                    self.pos_tag_counts_[jump.get(tag, "other")] += 1