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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 var_quadratic_sum(A, C, H, beta, x0):
 r""" Computes the expected discounted quadratic sum .. math:: q(x_0) = \mathbb{E} \Big[ \sum_{t=0}^{\infty} \beta^t x_t' H x_t \Big] Here :math:`{x_t}` is the VAR process :math:`x_{t+1} = A x_t + C w_t` with :math:`{x_t}` standard normal and :math:`x_0` the initial condition. Parameters A : array_like(float, ndim=2) The matrix described above in description. Should be n x n C : array_like(float, ndim=2) The matrix described above in description. Should be n x n H : array_like(float, ndim=2) The matrix described above in description. Should be n x n beta: scalar(float) Should take a value in (0, 1) x_0: array_like(float, ndim=1) The initial condtion. A conformable array (of length n, or with n rows) Returns ------- q0: scalar(float) Represents the value :math:`q(x_0)` Remarks: The formula for computing :math:`q(x_0)` is :math:`q(x_0) = x_0' Q x_0 + v` where * :math:`Q` is the solution to :math:`Q = H + \beta A' Q A`, and * :math:`v = \frac{trace(C' Q C) \beta}{(1 - \beta)}` """

    # == Make sure that A, C, H and x0 are array_like == #

    A, C, H = list(map(np.atleast_2d, (A, C, H)))
    x0 = np.atleast_1d(x0)
    # == Start computations == #
    Q = scipy.linalg.solve_discrete_lyapunov(sqrt(beta) * A.T, H)
    cq = dot(dot(C.T, Q), C)
    v = np.trace(cq) * beta / (1 - beta)
    q0 = dot(dot(x0.T, Q), x0) + v

    return q0