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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 K_to_F(self, K, method='doubling'):
 """ Compute agent 1's best value-maximizing response F, given K. Parameters K : array_like(float, ndim=2) A j x n array method : str, optional(default='doubling') Solution method used in solving the associated Riccati equation, str in {'doubling', 'qz'}. Returns ------- F : array_like(float, ndim=2) The policy function for a given K P : array_like(float, ndim=2) The value function for a given K """

        A1 = self.A + dot(self.C, K)
        B1 = self.B
        Q1 = self.Q
        R1 = self.R - self.beta * self.theta * dot(K.T, K)
        lq = LQ(Q1, R1, A1, B1, beta=self.beta)
        P, F, d = lq.stationary_values(method=method)

        return F, P