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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 mean_rotor_in_chordal_metric(R, t=None):
 """Return rotor that is closest to all R in the least-squares sense This can be done (quasi-)analytically because of the simplicity of the chordal metric function. The only approximation is the simple 2nd-order discrete formula for the definite integral of the input rotor function. Note that the `t` argument is optional. If it is present, the times are used to weight the corresponding integral. If it is not present, a simple sum is used instead (which may be slightly faster). """

    if not t:
        return np.quaternion(*(np.sum(as_float_array(R)))).normalized()
    mean = np.empty((4,), dtype=float)
    definite_integral(as_float_array(R), t, mean)
    return np.quaternion(*mean).normalized()