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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 compute_err(self, solution_y, coefficients):
 """ Return an error value by finding the absolute difference for each element in a list of solution-generated y-values versus expected values. Compounds error by 50% for each negative coefficient in the solution. solution_y: list of y-values produced by a solution coefficients: list of polynomial coefficients represented by the solution return: error value """

        error = 0
        for modeled, expected in zip(solution_y, self.expected_values):
            error += abs(modeled - expected)

        if any([c < 0 for c in coefficients]):
            error *= 1.5

        return error