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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 geometric_sums(self, beta, x_t):
 r""" Forecast the geometric sums .. math:: S_x := E \Big[ \sum_{j=0}^{\infty} \beta^j x_{t+j} | x_t \Big] S_y := E \Big[ \sum_{j=0}^{\infty} \beta^j y_{t+j} | x_t \Big] Parameters beta : scalar(float) Discount factor, in [0, 1) beta : array_like(float) The term x_t for conditioning Returns ------- S_x : array_like(float) Geometric sum as defined above S_y : array_like(float) Geometric sum as defined above """


        I = np.identity(self.n)
        S_x = solve(I - beta * self.A, x_t)
        S_y = self.G.dot(S_x)

        return S_x, S_y