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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_B_statistics(self, K, W, log_concave, *args, **kwargs):
 """ Rasmussen suggests the use of a numerically stable positive definite matrix B Which has a positive diagonal element and can be easyily inverted :param K: Prior Covariance matrix evaluated at locations X :type K: NxN matrix :param W: Negative hessian at a point (diagonal matrix) :type W: Vector of diagonal values of hessian (1xN) :returns: (K_Wi_i, L_B, not_provided) """

        #w = GPy.util.diag.view(W)
        #W[:] = np.where(w<1e-6, 1e-6, w)

        #B = I + KW
        B = np.eye(K.shape[0]) + np.dot(K, W)
        #Bi, L, Li, logdetB = pdinv(B)
        Bi = np.linalg.inv(B)

        #K_Wi_i = np.eye(K.shape[0]) - mdot(W, Bi, K)
        K_Wi_i = np.dot(W, Bi)

        #self.K_Wi_i_brute = np.linalg.inv(K + np.linalg.inv(W))
        #self.B = B
        #self.Bi = Bi
        Ki_W_i = np.dot(Bi, K)

        sign, logdetB = np.linalg.slogdet(B)
        return K_Wi_i, sign*logdetB, Bi, Ki_W_i