input: Write a function to optimize the given objective function using the scipy.optimize.minimize method with the Nelder-Mead algorithm. The function should return the optimized parameters.
  
  
  from scipy.optimize import minimize 
  
  def obj_func(x):
      # objective function
      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
  
  print(optimize())
  
  
  assert list(optimize()) == [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