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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 normalized_table_calc(classes, table):
 """ Return normalized confusion matrix. :param classes: classes list :type classes:list :param table: table :type table:dict :return: normalized table as dict """

    map_dict = {k: 0 for k in classes}
    new_table = {k: map_dict.copy() for k in classes}
    for key in classes:
        div = sum(table[key].values())
        if div == 0:
            div = 1
        for item in classes:
            new_table[key][item] = numpy.around(table[key][item] / div, 5)
    return new_table