def METHOD_NAME():
    n = 5
    def makec(inputs, params):
        c = tc.Circuit(n)
        for i in range(n):
            c.rx(i, theta=inputs[i])
        for i in range(n):
            c.rz(i, theta=params[0, i])
        for i in range(n - 1):
            c.cx(i, i + 1)
        for i in range(n):
            c.rx(i, theta=params[1, i])
        return c
    pss = []
    for i in range(n):
        ps = [0 for _ in range(n)]
        ps[i] = 3
        pss.append(ps)
    def exp_val(c, device=None):
        rs = tc.cloud.wrapper.batch_expectation_ps(c, pss, device)
        return tc.backend.stack(rs)
    def qmlf(inputs, params, device=None):
        c = makec(inputs, params)
        return exp_val(c, device)
    inputs = tc.array_to_tensor(np.array([0, 1, 0, 1, 0]))
    params = np.ones([2, n])
    print(qmlf(inputs, params, device="9gmon"))
    print(qmlf(inputs, params))