input: Write a function to optimize a given objective function using the scipy.optimize.minimize method. The function should set up the optimization problem, provide initial guesses for the variables, and return the optimized values of the variables.
  
  
  from scipy.optimize import minimize 
  
  def obj_func(x):
      # objective function
      return (x[0]**2 + x[1]**2 + x[2]**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 optimize().tolist() == [0.0, 0.0, 0.0]
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