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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 PorodGuinierMulti(q, A, *alphasRgs):
 """Empirical multi-part Porod-Guinier scattering Inputs: ------- ``q``: independent variable ``A``: factor for the first Power-law-branch gyration and power-law exponents of the consecutive parts Formula: -------- The intensity is a piecewise function with continuous first derivatives. The separating points in ``q`` between the consecutive parts and the intensity factors of them (except the first) are determined from conditions of smoothness (continuity of the function and its first derivative) at the border points of the intervals. Guinier-type (``G*exp(-q^2*Rg1^2/3)``) and Power-law type (``A*q^alpha``) parts follow each other in alternating sequence. Literature: B. Hammouda: A new Guinier-Porod model. J. Appl. Crystallogr. (2010) 43, 716-719. """

    scalefactor = A
    funcs = [lambda q: Powerlaw(q, A, alphasRgs[0])]
    indices = np.ones_like(q, dtype=np.bool)
    constraints = []
    for i in range(1, len(alphasRgs)):
        if i % 2:
            # alphasRgs[i] is a radius of gyration, alphasRgs[i-1] is a power-law exponent
            qsep = _PGgen_qsep(alphasRgs[i - 1], alphasRgs[i], 3)
            scalefactor = _PGgen_G(alphasRgs[i - 1], alphasRgs[i], 3, scalefactor)
            funcs.append(lambda q, G=scalefactor, Rg=alphasRgs[i]: Guinier(q, G, Rg))
        else:
            # alphasRgs[i] is an exponent, alphasRgs[i-1] is a radius of gyration
            qsep = _PGgen_qsep(alphasRgs[i], alphasRgs[i - 1], 3)
            scalefactor = _PGgen_A(alphasRgs[i], alphasRgs[i - 1], 3, scalefactor)
            funcs.append(lambda q, a=scalefactor, alpha=alphasRgs[i]: a * q ** alpha)
        # this belongs to the previous
        constraints.append(indices & (q < qsep))
        indices[q < qsep] = False
    constraints.append(indices)
    return np.piecewise(q, constraints, funcs)