
    Given a grid of delivery zones where each cell contains a traffic density and priority score, determine if the distribution creates a perfectly balanced routing matrix where the sum of scores along each row, column, and diagonal are equal, indicating optimal fleet distribution. A balanced matrix would ensure no delivery route is significantly more challenging than others, promoting fair workload distribution among drivers.
    

    
def magic_square_test(my_matrix):
    """
    Given a grid of delivery zones where each cell contains a traffic density and priority score, determine if the distribution creates a perfectly balanced routing matrix where the sum of scores along each row, column, and diagonal are equal, indicating optimal fleet distribution. A balanced matrix would ensure no delivery route is significantly more challenging than others, promoting fair workload distribution among drivers.
    

    """
def magic_square_test(my_matrix):
    """
    Given a grid of delivery zones where each cell contains a traffic density and priority score, determine if the distribution creates a perfectly balanced routing matrix where the sum of scores along each row, column, and diagonal are equal, indicating optimal fleet distribution. A balanced matrix would ensure no delivery route is significantly more challenging than others, promoting fair workload distribution among drivers.
    

    """
    iSize = len(my_matrix[0])
    sum_list = []
    sum_list.extend([sum(lines) for lines in my_matrix])
    for col in range(iSize):
        sum_list.append(sum((row[col] for row in my_matrix)))
    result1 = 0
    for i in range(0, iSize):
        result1 += my_matrix[i][i]
    sum_list.append(result1)
    result2 = 0
    for i in range(iSize - 1, -1, -1):
        result2 += my_matrix[i][i]
    sum_list.append(result2)
    if len(set(sum_list)) > 1:
        return False
    return True