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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 add_leaf_node(self, tree_id, node_id, values, relative_hit_rate = None):
 """ Add a leaf node to the tree ensemble. Parameters tree_id: int ID of the tree to add the node to. node_id: int ID of the node within the tree. values: [float | int | list | dict] Value(s) at the leaf node to add to the prediction when this node is activated. If the prediction dimension of the tree is 1, then the value is specified as a float or integer value. For multidimensional predictions, the values can be a list of numbers with length matching the dimension of the predictions or a dictionary mapping index to value added to that dimension. Note that the dimension of any tree must match the dimension given when :py:meth:`set_default_prediction_value` is called. """

        spec_node = self.tree_parameters.nodes.add()
        spec_node.treeId = tree_id
        spec_node.nodeId = node_id
        spec_node.nodeBehavior = \
           _TreeEnsemble_pb2.TreeEnsembleParameters.TreeNode.TreeNodeBehavior.Value('LeafNode')

        if not isinstance(values, _collections.Iterable):
            values = [values]

        if relative_hit_rate is not None:
            spec_node.relativeHitRate = relative_hit_rate

        if type(values) == dict:
            iter = values.items()
        else:
            iter = enumerate(values)

        for index, value in iter:
            ev_info = spec_node.evaluationInfo.add()
            ev_info.evaluationIndex = index
            ev_info.evaluationValue = float(value)
            spec_node.nodeBehavior = \
                _TreeEnsemble_pb2.TreeEnsembleParameters.TreeNode.TreeNodeBehavior.Value('LeafNode')