input: from scipy.optimize import minimize 
  
  def obj_func(x):
      """
      Returns the value of the objective function evaluated at the given point x.
      
      Example:
      >>> obj_func([1.0, 2.0, 3.0])
      14.0
      """
      return x[0]**2 + x[1]**2 + x[2]**2
  
  def optimize(): 
      """
      Optimizes the objective function using the Nelder-Mead method.
      
      Example:
      >>> optimize()
      [-3.78260326e-09, -2.78237859e-09, -4.29277060e-09]
      """
      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:     x0 = [0.0, 0.0, 0.0] 
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
      return res.x