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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 derivLogCdfNormal(z):
 """ Robust implementations of derivative of the log cdf of a standard normal. @see [[https://github.com/mseeger/apbsint/blob/master/src/eptools/potentials/SpecfunServices.h original implementation]] in C from Matthias Seeger. """

    if (abs(z) < ERF_CODY_LIMIT1):
        # Phi(z) approx (1 + y R_3(y^2))/2, y = z/sqrt(2)
        return 2.0 * np.exp(logPdfNormal(z)) / (1.0 + (z / M_SQRT2) * _erfRationalHelperR3(0.5 * z * z))
    elif (z < 0.0):
        # Phi(z) approx N(z) Q(-z)/(-z), z<0
        return -z / _erfRationalHelper(-z)
    else:
        t = np.exp(logPdfNormal(z))
        return t / (1.0 - t * _erfRationalHelper(z) / z)