input: Write a function to optimize the given objective function using the scipy.optimize.minimize function with the Nelder-Mead method. The function should return the optimized values of x.
  assert optimize()==(some desired optimized value)
output: def optimize():
      from scipy.optimize import minimize
      def objective(x):
          return (x[0] - 1)**2 + (x[1] - 2.5)**2
      result = minimize(objective, [2, 0], method='Nelder-Mead')
      return result.x