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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 tag(self, corpus, tokenize=True):
        '''Tags a string `corpus`.'''
        # Assume untokenized corpus has \n between sentences and ' ' between words
        s_split = SentenceTokenizer().tokenize if tokenize else lambda t: t.split('\n')
        w_split = WordTokenizer().tokenize if tokenize else lambda s: s.split()
        def split_sents(corpus):
            for s in s_split(corpus):
                yield w_split(s)

        prev, prev2 = self.START
        tokens = []
        for words in split_sents(corpus):
            context = self.START + [self._normalize(w) for w in words] + self.END
            for i, word in enumerate(words):
                tag = self.tagdict.get(word)
                if not tag:
                    features = self._get_features(i, word, context, prev, prev2)
                    tag = self.model.predict(features)
                tokens.append((word, tag))
                prev2 = prev
                prev = tag
        return tokens