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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 kde(data, bw, cmap='hot', method='hist', scaling='sqrt', alpha=220, cut_below=None, clip_above=None, binsize=1, cmap_levels=10, show_colorbar=False):
 """ Kernel density estimation visualization :param data: data access object :param bw: kernel bandwidth (in screen coordinates) :param cmap: colormap :param method: if kde use KDEMultivariate from statsmodel, which provides a more accurate but much slower estimation. If hist, estimates density applying gaussian smoothing on a 2D histogram, which is much faster but less accurate :param scaling: colorscale, lin log or sqrt :param alpha: color alpha :param cut_below: densities below cut_below are not drawn :param clip_above: defines the max value for the colorscale :param binsize: size of the bins for hist estimator :param cmap_levels: discretize colors into cmap_levels levels :param show_colorbar: show colorbar """

    from geoplotlib.layers import KDELayer
    _global_config.layers.append(KDELayer(data, bw, cmap, method, scaling, alpha,
                 cut_below, clip_above, binsize, cmap_levels, show_colorbar))