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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 __diff_internal(self):
 """Differentiate a B-spline once, and return the resulting coefficients and Bspline objects. This preserves the Bspline object nature of the data, enabling recursive implementation of higher-order differentiation (see `diff`). The value of the first derivative of `B` at a point `x` can be obtained as:: def diff1(B, x):
 terms = B.__diff_internal() return sum( ci*Bi(x) for ci,Bi in terms ) Returns: tuple of tuples, where each item is (coefficient, Bspline object). See: `diff`: differentiation of any order >= 0 """

        assert self.p > 0, "order of Bspline must be > 0"  # we already handle the other case in diff()

        # https://www.cs.mtu.edu/~shene/COURSES/cs3621/NOTES/spline/B-spline/bspline-derv.html
        #
        t    = self.knot_vector
        p    = self.p
        Bi   = Bspline( t[:-1], p-1 )
        Bip1 = Bspline( t[1:],  p-1 )

        numer1 = +p
        numer2 = -p
        denom1 = t[p:-1]   - t[:-(p+1)]
        denom2 = t[(p+1):] - t[1:-p]

        with np.errstate(divide='ignore', invalid='ignore'):
            ci   = np.where(denom1 != 0., (numer1 / denom1), 0.)
            cip1 = np.where(denom2 != 0., (numer2 / denom2), 0.)

        return ( (ci,Bi), (cip1,Bip1) )