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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 to_networkx(self):
 r"""Export the graph to NetworkX. Edge weights are stored as an edge attribute, under the name "weight". Signals are stored as node attributes, under their name in the :attr:`signals` dictionary. `N`-dimensional signals are broken into `N` 1-dimensional signals. They will eventually be joined back together on import. Returns ------- graph : :class:`networkx.Graph` A NetworkX graph object. See Also -------- to_graphtool : export to graph-tool save : save to a file Examples -------- Name: Path Type: DiGraph Number of nodes: 4 Number of edges: 3 Average in degree: 0.7500 Average out degree: 0.7500 True NodeView((0, 1, 2, 3)) OutEdgeView([(0, 1), (1, 2), (2, 3)]) {'signal': 2.3} {'weight': 1.0} Another common goal is to use NetworkX to compute some properties to be be imported back in the PyGSP as signals. """

        nx = _import_networkx()

        def convert(number):
            # NetworkX accepts arbitrary python objects as attributes, but:
            # * the GEXF writer does not accept any NumPy types (on signals),
            # * the GraphML writer does not accept NumPy ints.
            if issubclass(number.dtype.type, (np.integer, np.bool_)):
                return int(number)
            else:
                return float(number)

        def edges():
            for source, target, weight in zip(*self.get_edge_list()):
                yield int(source), int(target), {'weight': convert(weight)}

        def nodes():
            for vertex in range(self.n_vertices):
                signals = {name: convert(signal[vertex])
                           for name, signal in self.signals.items()}
                yield vertex, signals

        self._break_signals()
        graph = nx.DiGraph() if self.is_directed() else nx.Graph()
        graph.add_nodes_from(nodes())
        graph.add_edges_from(edges())
        graph.name = self.__class__.__name__
        return graph