input: 
  from scipy.optimize import minimize
  
  def obj_func(x):
      """ Define an objective function that needs to be optimized.
      >>> obj_func([1.0, 2.0, 3.0])
      # This should return a value corresponding to the objective function's evaluation at (1.0, 2.0, 3.0)
      >>> obj_func([0.0, 0.0, 0.0])
      # This should return a value corresponding to the objective function's evaluation at (0.0, 0.0, 0.0)
      """
      return sum(x)  # Example objective function, replace with actual one
  
  def optimize():
      """ Optimize the objective function using a specific method.
      >>> optimize()
      # This should return an array of optimized values for the variables in the objective function
      """
      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