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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 contour(h, axes=None, zoom=None, label_contour=False, **kwargs):
 """ Draw a matplotlib contour plot from a 2D ROOT histogram. Parameters h : Hist2D A rootpy Hist2D axes : matplotlib Axes instance, optional (default=None) The axes to plot on. If None then use the global current axes. zoom : float or sequence, optional (default=None) The zoom factor along the axes. If a float, zoom is the same for each axis. If a sequence, zoom should contain one value for each axis. The histogram is zoomed using a cubic spline interpolation to create smooth contours. label_contour : Boolean, optional (default=False) If True, labels are printed on the contour lines. kwargs : additional keyword arguments, optional Additional keyword arguments are passed directly to matplotlib's contour function. Returns ------- Returns the value from matplotlib's contour function. """

    if axes is None:
        axes = plt.gca()
    x = np.array(list(h.x()))
    y = np.array(list(h.y()))
    z = np.array(h.z()).T
    if zoom is not None:
        from scipy import ndimage
        if hasattr(zoom, '__iter__'):
            zoom = list(zoom)
            x = ndimage.zoom(x, zoom[0])
            y = ndimage.zoom(y, zoom[1])
        else:
            x = ndimage.zoom(x, zoom)
            y = ndimage.zoom(y, zoom)
        z = ndimage.zoom(z, zoom)
    return_values = axes.contour(x, y, z, **kwargs)
    if label_contour:
        plt.clabel(return_values)
    return return_values