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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 resample_spline(points, smooth=.001, count=None, degree=3):
 """ Resample a path in space, smoothing along a b-spline. Parameters points: (n, dimension) float, points in space smooth: float, smoothing amount count: number of samples in output degree: int, degree of spline polynomial Returns --------- resampled: (count, dimension) float, points in space """

    from scipy.interpolate import splprep, splev
    if count is None:
        count = len(points)
    points = np.asanyarray(points)
    closed = np.linalg.norm(points[0] - points[-1]) < tol.merge

    tpl = splprep(points.T, s=smooth, k=degree)[0]
    i = np.linspace(0.0, 1.0, count)
    resampled = np.column_stack(splev(i, tpl))

    if closed:
        shared = resampled[[0, -1]].mean(axis=0)
        resampled[0] = shared
        resampled[-1] = shared

    return resampled