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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 train(self, sentences, save_loc=None, nr_iter=5):
        '''Train a model from sentences, and save it at ``save_loc``. ``nr_iter``
        controls the number of Perceptron training iterations.

        :param sentences: A list of (words, tags) tuples.
        :param save_loc: If not ``None``, saves a pickled model in this location.
        :param nr_iter: Number of training iterations.
        '''
        self._make_tagdict(sentences)
        self.model.classes = self.classes
        for iter_ in range(nr_iter):
            c = 0
            n = 0
            for words, tags in sentences:
                prev, prev2 = self.START
                context = self.START + [self._normalize(w) for w in words] \
                                                                    + self.END
                for i, word in enumerate(words):
                    guess = self.tagdict.get(word)
                    if not guess:
                        feats = self._get_features(i, word, context, prev, prev2)
                        guess = self.model.predict(feats)
                        self.model.update(tags[i], guess, feats)
                    prev2 = prev
                    prev = guess
                    c += guess == tags[i]
                    n += 1
            random.shuffle(sentences)
            logging.info("Iter {0}: {1}/{2}={3}".format(iter_, c, n, _pc(c, n)))
        self.model.average_weights()
        # Pickle as a binary file
        if save_loc is not None:
            pickle.dump((self.model.weights, self.tagdict, self.classes),
                         open(save_loc, 'wb'), -1)
        return None