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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 cross_entropy_calc(TOP, P, POP):
 """ Calculate cross entropy. :param TOP: test outcome positive :type TOP : dict :param P: condition positive :type P : dict :param POP: population :type POP : dict :return: cross entropy as float """

    try:
        result = 0
        for i in TOP.keys():
            reference_likelihood = P[i] / POP[i]
            response_likelihood = TOP[i] / POP[i]
            if response_likelihood != 0 and reference_likelihood != 0:
                result += reference_likelihood * \
                    math.log(response_likelihood, 2)
        return -result
    except Exception:
        return "None"