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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 conditional_entropy_calc(classes, table, P, POP):
 """ Calculate conditional entropy. :param classes: confusion matrix classes :type classes : list :param table: confusion matrix table :type table : dict :param P: condition positive :type P : dict :param POP: population :type POP : dict :return: conditional entropy as float """

    try:
        result = 0
        for i in classes:
            temp = 0
            for index, j in enumerate(classes):
                p_prime = 0
                if P[i] != 0:
                    p_prime = table[i][j] / P[i]
                if p_prime != 0:
                    temp += p_prime * math.log(p_prime, 2)
            result += temp * (P[i] / POP[i])
        return -result
    except Exception:
        return "None"