input: from scipy.optimize import minimize 
  
  def obj_func(x):
      """Objective function to be optimized.
      >>> optimize()
      array([...]), # The actual output may vary based on the implementation of obj_func
      """
  
  def optimize(): 
      """Set up optimization problem and return the optimal solution.
      >>> optimize()
      array([0.0, 0.0, 0.0]), # Example output assuming obj_func is set to return zero for x = [0.0, 0.0, 0.0]
      """
      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