input: Write a function to optimize the given objective function using the scipy.optimize.minimize method. The objective function should be defined within the function and the optimization should be performed using the Nelder-Mead method. The function should return the optimal values of the variables.
  
  from scipy.optimize import minimize
  
  def obj_func(x):
      # objective function definition here
      return (x[0] - 1)**2 + (x[1] - 2.5)**2
  
  def optimize():
      x0 = [0.0, 0.0]
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x
  
  assert list(optimize()) == [1.0, 2.5]
  assert list(optimize()) != [0.0, 0.0]
  assert list(optimize()) != [2.0, 3.5]
output: def optimize():
      x0 = [0.0, 0.0]
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x