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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 GeneralGuinierPorod(q, factor, *args, **kwargs):
 """Empirical generalized multi-part Guinier-Porod scattering Inputs: ------- ``q``: independent variable ``factor``: factor for the first branch other arguments (*args):
 the defining arguments of the consecutive parts: radius of gyration (``Rg``) and dimensionality parameter (``s``) for Guinier and exponent (``alpha``) for power-law parts. supported keyword arguments: ``startswithguinier``: True if the first segment is a Guinier-type scattering (this is the default) or False if it is a power-law 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*q**(3-s)*exp(-q^2*Rg1^2/s)``) and Power-law type (``A*q^alpha``) parts follow each other in alternating sequence. The exact number of parts is determined from the number of positional arguments (*args). Literature: B. Hammouda: A new Guinier-Porod model. J. Appl. Crystallogr. (2010) 43, 716-719. """

    if kwargs.get('startswithguinier', True):
        funcs = [lambda q, A = factor:GeneralGuinier(q, A, args[0], args[1])]
        i = 2
        guiniernext = False
    else:
        funcs = [lambda q, A = factor: Powerlaw(q, A, args[0])]
        i = 1
        guiniernext = True
    indices = np.ones_like(q, dtype=np.bool)
    constraints = []
    while i < len(args):
        if guiniernext:
            # args[i] is a radius of gyration, args[i+1] is a dimensionality parameter, args[i-1] is a power-law exponent
            qsep = _PGgen_qsep(args[i - 1], args[i], args[i + 1])
            factor = _PGgen_G(args[i - 1], args[i], args[i + 1], factor)
            funcs.append(lambda q, G=factor, Rg=args[i], s=args[i + 1]: GeneralGuinier(q, G, Rg, s))
            guiniernext = False
            i += 2
        else:
            # args[i] is an exponent, args[i-2] is a radius of gyration, args[i-1] is a dimensionality parameter
            qsep = _PGgen_qsep(args[i], args[i - 2], args[i - 1])
            factor = _PGgen_A(args[i], args[i - 2], args[i - 1], factor)
            funcs.append(lambda q, a=factor, alpha=args[i]: a * q ** alpha)
            guiniernext = True
            i += 1
        # this belongs to the previous
        constraints.append(indices & (q < qsep))
        indices[q < qsep] = False
    constraints.append(indices)
    return np.piecewise(q, constraints, funcs)