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 an initial guess (x0) and return the optimized values for the variables.
  
  from scipy.optimize import minimize 
  
  def obj_func(x):
      # objective function
      return (x[0] - 1)**2 + (x[1] - 2.5)**2 + (x[2] - 0.5)**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())
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