input: from scipy.optimize import minimize
  
  def optimize_func(x: list) -> float:
      """ Optimize the given objective function using the minimize function from scipy.
      The objective function is defined within the function.
      >>> print(optimize())
      [1, 1, 1]
      """
      
      def obj_func(x):
          # objective function
          return x[0]**2 + x[1]**2 + x[2]**2
      
      def optimize(): 
          # set up optimization problem
          x0 = [0.0, 0.0, 0.0] 
          res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
          return res.x
      
      return optimize()
output:     def optimize(): 
          # set up optimization problem
          x0 = [0.0, 0.0, 0.0] 
          res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
          return res.x
      
      return optimize()