input: Write a function that optimizes the given objective function using the minimize function from the scipy.optimize module. The objective function should be implemented within the obj_func function, and the optimization process should be set up in the optimize function. The initialize values should be 0 for three variables, and the optimization method should be 'nelder-mead'. The result should then be printed.
  
  from scipy.optimize import minimize 
  
  def obj_func(x):
      # objective function
      # ...
  
  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
  
  print(optimize())
  
  assert list(optimize()) == [0.0, 0.0, 0.0]
output: def obj_func(x):
      return x[0]**2 + x[1]**2 + x[2]**2
  
  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