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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 discretize_bspline(control, knots, count=None, scale=1.0):
 """ Given a B-Splines control points and knot vector, return a sampled version of the curve. Parameters control : (o, d) float Control points of the b- spline knots : (j,) float B-spline knots count : int Number of line segments to discretize the spline If not specified will be calculated as something reasonable Returns discrete : (count, dimension) float Points on a polyline version of the B-spline """


    # evaluate the b-spline using scipy/fitpack
    from scipy.interpolate import splev
    # (n, d) control points where d is the dimension of vertices
    control = np.asanyarray(control, dtype=np.float64)
    degree = len(knots) - len(control) - 1
    if count is None:
        norm = np.linalg.norm(np.diff(control, axis=0), axis=1).sum()
        count = int(np.clip(norm / (res.seg_frac * scale),
                            res.min_sections * len(control),
                            res.max_sections * len(control)))

    ipl = np.linspace(knots[0], knots[-1], count)
    discrete = splev(ipl, [knots, control.T, degree])
    discrete = np.column_stack(discrete)

    return discrete