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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 _logL_delays(self, delays_model, delays_measured, delays_errors):
 """ log likelihood of modeled delays vs measured time delays under considerations of errors :param delays_model: n delays of the model (not relative delays) :param delays_measured: relative delays (1-2,1-3,1-4) relative to the first in the list :param delays_errors: gaussian errors on the measured delays :return: log likelihood of data given model """

        delta_t_model = np.array(delays_model[1:]) - delays_model[0]
        logL = np.sum(-(delta_t_model - delays_measured) ** 2 / (2 * delays_errors ** 2))
        return logL