def prettify(self, tokens: List[str], tags: List[str], lemmas: List[str]) -> Union[List[str], str]:
        """Prettifies output of morphological tagger.
        Args:
            tokens: tokenized source sentence
            tags: list of tags, the output of a tagger
            lemmas: list of lemmas, the output of a lemmatizer
        Returns:
            the prettified output of the tagger.
        Examples:
            >>> sent = "John really likes pizza .".split()
            >>> tags = ["PROPN,Number=Sing", "ADV",
            >>>         "VERB,Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin",
            >>>         "NOUN,Number=Sing", "PUNCT"]
            >>> lemmas = "John really like pizza .".split()
            >>> prettifier = LemmatizedOutputPrettifier()
            >>> self.prettify(sent, tags, lemmas)
                1	John	John	PROPN	_	Number=Sing	_	_	_	_
                2	really	really	ADV	_	_	_	_	_	_
                3	likes	like	VERB	_	Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin	_	_	_	_
                4	pizza	pizza	NOUN	_	Number=Sing	_	_	_	_
                5	.	.	PUNCT	_	_	_	_	_	_
        """
        answer = []
        for i, (word, tag, lemma) in enumerate(zip(tokens, tags, lemmas)):
            pos, tag = make_pos_and_tag(tag, sep=",")
            answer.append(self.format_string.format(i + 1, word, lemma, pos, tag))
        if self.return_string:
            answer = self.begin + self.sep.join(answer) + self.end
        return answer
Prettifies output of morphological tagger.
        Args:
            tokens: tokenized source sentence
            tags: list of tags, the output of a tagger
            lemmas: list of lemmas, the output of a lemmatizer
        Returns:
            the prettified output of the tagger.
        Examples:
            >>> sent = "John really likes pizza .".split()
            >>> tags = ["PROPN,Number=Sing", "ADV",
            >>>         "VERB,Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin",
            >>>         "NOUN,Number=Sing", "PUNCT"]
            >>> lemmas = "John really like pizza .".split()
            >>> prettifier = LemmatizedOutputPrettifier()
            >>> self.prettify(sent, tags, lemmas)
                1	John	John	PROPN	_	Number=Sing	_	_	_	_
                2	really	really	ADV	_	_	_	_	_	_
                3	likes	like	VERB	_	Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin	_	_	_	_
                4	pizza	pizza	NOUN	_	Number=Sing	_	_	_	_
                5	.	.	PUNCT	_	_	_	_	_	_
        
deeppavlov/models/morpho_syntax_parser/syntax_parsing.py