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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 graph_to_laplacian(G, normalized=True):
 """ Converts a graph from popular Python packages to Laplacian representation. Currently support NetworkX, graph_tool and igraph. Parameters G : obj Input graph normalized : bool Whether to use normalized Laplacian. Normalized and unnormalized Laplacians capture different properties of graphs, e.g. normalized Laplacian spectrum can determine whether a graph is bipartite, but not the number of its edges. We recommend using normalized Laplacian. Returns ------- scipy.sparse Laplacian matrix of the input graph Examples -------- [[ 2, -1, -1], [-1, 2, -1], [-1, -1, 2]] None """

    try:
        import networkx as nx
        if isinstance(G, nx.Graph):
            if normalized:
                return nx.normalized_laplacian_matrix(G)
            else:
                return nx.laplacian_matrix(G)
    except ImportError:
        pass
    try:
        import graph_tool.all as gt
        if isinstance(G, gt.Graph):
            if normalized:
                return gt.laplacian_type(G, normalized=True)
            else:
                return gt.laplacian(G)
    except ImportError:
        pass
    try:
        import igraph as ig
        if isinstance(G, ig.Graph):
            if normalized:
                return np.array(G.laplacian(normalized=True))
            else:
                return np.array(G.laplacian())
    except ImportError:
        pass