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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 draw_qubit_graph(G, layout, linear_biases={}, quadratic_biases={}, nodelist=None, edgelist=None, cmap=None, edge_cmap=None, vmin=None, vmax=None, edge_vmin=None, edge_vmax=None, **kwargs):
 """Draws graph G according to layout. If `linear_biases` and/or `quadratic_biases` are provided, these are visualized on the plot. Parameters G : NetworkX graph The graph to be drawn layout : dict A dict of coordinates associated with each node in G. Should treated as vectors, and should all have the same length. linear_biases : dict (optional, default {}) A dict of biases associated with each node in G. Should be of quadratic_biases : dict (optional, default {}) A dict of biases associated with each edge in G. Should be of edges (i.e., :math:`i=j`) are treated as linear biases. kwargs : optional keywords See networkx.draw_networkx() for a description of optional keywords, with the exception of the `pos` parameter which is not used by this function. If `linear_biases` or `quadratic_biases` are provided, any provided `node_color` or `edge_color` arguments are ignored. """


    if linear_biases or quadratic_biases:
        # if linear biases and/or quadratic biases are provided, then color accordingly.

        try:
            import matplotlib.pyplot as plt
            import matplotlib as mpl
        except ImportError:
            raise ImportError("Matplotlib and numpy required for draw_qubit_graph()")

        if nodelist is None:
            nodelist = G.nodes()

        if edgelist is None:
            edgelist = G.edges()

        if cmap is None:
            cmap = plt.get_cmap('coolwarm')

        if edge_cmap is None:
            edge_cmap = plt.get_cmap('coolwarm')

        # any edges or nodes with an unspecified bias default to 0
        def edge_color(u, v):
            c = 0.
            if (u, v) in quadratic_biases:
                c += quadratic_biases[(u, v)]
            if (v, u) in quadratic_biases:
                c += quadratic_biases[(v, u)]
            return c

        def node_color(v):
            c = 0.
            if v in linear_biases:
                c += linear_biases[v]
            if (v, v) in quadratic_biases:
                c += quadratic_biases[(v, v)]
            return c

        node_color = [node_color(v) for v in nodelist]
        edge_color = [edge_color(u, v) for u, v in edgelist]

        kwargs['edge_color'] = edge_color
        kwargs['node_color'] = node_color

        # the range of the color map is shared for nodes/edges and is symmetric
        # around 0.
        vmag = max(max(abs(c) for c in node_color), max(abs(c) for c in edge_color))
        if vmin is None:
            vmin = -1 * vmag
        if vmax is None:
            vmax = vmag
        if edge_vmin is None:
            edge_vmin = -1 * vmag
        if edge_vmax is None:
            edge_vmax = vmag

    draw(G, layout, nodelist=nodelist, edgelist=edgelist,
         cmap=cmap, edge_cmap=edge_cmap, vmin=vmin, vmax=vmax, edge_vmin=edge_vmin,
         edge_vmax=edge_vmax,
         **kwargs)

    # if the biases are provided, then add a legend explaining the color map
    if linear_biases or quadratic_biases:
        fig = plt.figure(1)
        # cax = fig.add_axes([])
        cax = fig.add_axes([.9, 0.2, 0.04, 0.6])  # left, bottom, width, height
        mpl.colorbar.ColorbarBase(cax, cmap=cmap,
                                  norm=mpl.colors.Normalize(vmin=-1 * vmag, vmax=vmag, clip=False),
                                  orientation='vertical')