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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 entropy_calc(item, POP):
 """ Calculate reference and response likelihood. :param item : TOP or P :type item : dict :param POP: population :type POP : dict :return: reference or response likelihood as float """

    try:
        result = 0
        for i in item.keys():
            likelihood = item[i] / POP[i]
            if likelihood != 0:
                result += likelihood * math.log(likelihood, 2)
        return -result
    except Exception:
        return "None"