How to use the simpleai.machine_learning.classifiers.DecisionTreeNode function in simpleai

def learn(self):
        if not self.attributes:
            self.root = self._single_node_tree()
            return
        self.root = DecisionTreeNode()
        q = [(self.root, self.dataset)]
        while q:
            node, examples = q.pop()
            A = self._max_gain_split(examples)
            counts = A.get_target_class_counts()
            branches = A.get_branches()

            # Base case exception
            if node is self.root:
                node.set_results_from_counts(counts)

            if len(counts) == 1:
                continue  # Avoid splitting when there's a single target class
            if len(branches) == 1:
                continue  # Avoid splitting when there's a single child branch

def _single_node_tree(self):
        c = Counter(self.target)
        for example in self.dataset:
            c.add(example)
        node = DecisionTreeNode()
        node.set_results_from_counts(c)
        return node

def learn(self, examples, attributes, parent_examples):
        """
        A decision tree learner that *strictly* follows the pseudocode given in
        AIMA. In 3rd edition, see Figure 18.5, page 702.
        """
        if not examples:
            return self.plurality_value(parent_examples)
        elif len(set(map(self.target, examples))) == 1:
            return self.plurality_value(examples)
        elif not attributes:
            return self.plurality_value(examples)
        A = max(attributes, key=lambda a: self.importance(a, examples))
        tree = DecisionTreeNode(attribute=A)
        for value in set(map(A, examples)):
            exs = [e for e in examples if A(e) == value]
            subtree = self.learn(exs, attributes - set([A]), examples)
            tree.add_branch(value, subtree)
        return tree

def plurality_value(self, examples):
        if not examples:
            raise ValueError("Dataset is empty")
        counter = Counter(self.target)
        for example in examples:
            counter.add(example)
        tree = DecisionTreeNode()
        # Note that tie is *not* solved randomly here
        tree.set_results_from_counts(counter)
        return tree

def learn(self):
        if not self.attributes:
            self.root = self._single_node_tree()
            return
        self.root = DecisionTreeNode()
        leaves = {self.root: self._new_set_of_gain_counters()}
        while leaves:
            leaf = None
            for example in self.dataset:
                leaf = walk_to_leaf(self.root, example)
                if leaf not in leaves:
                    continue  # Don't split leaves that where ignored
                for gain_counter in leaves[leaf]:
                    gain_counter.add(example)
            if leaf is None:
                raise ValueError("Dataset is empty")

            old_leaves = leaves
            leaves = {}
            for leaf, gains in old_leaves.iteritems():
                winner = max(gains, key=lambda gain: gain.get_gain())