input: Write a function to optimize the given objective function using the scipy.optimize.minimize function. Define the objective function within this function and set up the optimization problem with appropriate initial guesses and options. Return the optimized parameters found by the minimize function.
  
  def optimize(): 
      def obj_func(x):
          # objective function
          return (x[0]-3)**2 + (x[1]-4)**2 + (x[2]-5)**2
      # 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
  
  assert list(optimize())==[3.0, 4.0, 5.0]
output: def optimize(): 
      def obj_func(x):
          return (x[0]-3)**2 + (x[1]-4)**2 + (x[2]-5)**2
      x0 = [0.0, 0.0, 0.0] 
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
      return res.x