input: Write a function to optimize the given objective function using the scipy.optimize.minimize method. The function should set up the optimization problem with initial guess x0 and return the optimized values of x.
  assert list(optimize())==[-2.0, 4.0, -1.0]
  assert list(optimize())==[-3.0, 2.0, 1.0]
  assert list(optimize())==[1.0, -5.0, 3.0]
  assert list(optimize())==[0.0, 0.0, 0.0]
  assert list(optimize())==[2.0, -3.0, -2.0]
output: def optimize():
      from scipy.optimize import minimize
      def objective(x):
          return (x[0] + 1)**2 + (x[1] - 2)**2 + (x[2] + 3)**2
      x0 = [0, 0, 0]
      result = minimize(objective, x0)
      return result.x