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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 hmcorrelation(sites_or_distances, imt, uncertainty_multiplier=0):
 """ Returns the Heresi-Miranda correlation model. :param sites_or_distances: SiteCollection instance o distance matrix :param imt: Intensity Measure Type (PGA or SA) :param uncertainty_multiplier: Value to be multiplied by the uncertainty in the correlation parameter beta. If uncertainty_multiplier = 0 (default), the median value is used as a constant value. """

    if hasattr(sites_or_distances, 'mesh'):
        distances = sites_or_distances.mesh.get_distance_matrix()
    else:
        distances = sites_or_distances

    period = imt.period

    # Eq. (9)
    if period < 1.37:
        Med_b = 4.231 * period * period - 5.180 * period + 13.392
    else:
        Med_b = 0.140 * period * period - 2.249 * period + 17.050

    # Eq. (10)
    Std_b = (4.63e-3 * period*period + 0.028 * period + 0.713)

    # Obtain realization of b
    if uncertainty_multiplier == 0:
        beta = Med_b
    else:
        beta = numpy.random.lognormal(
            numpy.log(Med_b), Std_b * uncertainty_multiplier)

    # Eq. (8)
    return numpy.exp(-numpy.power((distances / beta), 0.55))