input: Write a function to optimize the given objective function using the scipy.optimize.minimize method with initial guess x0 and method 'nelder-mead'. If the optimization is successful, return the optimized values of x.
  
  from scipy.optimize import minimize
  
  def obj_func(x):
      # objective function
      return (x[0] - 1)**2 + (x[1] - 2.5)**2 + (x[2] - 3.7)**2
  
  def optimize():
      # set up optimization problem
      x0 = [0.0, 0.0, 0.0]
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x
  
  print(optimize())
  
  assert list(optimize()) == [1.0, 2.5, 3.7]
output: def optimize():
      x0 = [0.0, 0.0, 0.0]
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x