input: Write a function to optimize the given objective function using the minimize function from scipy.optimize. The objective function is defined within the function obj_func. The optimization starts from an initial guess x0 and uses the 'nelder-mead' method with specific options. The function should return the optimal values of the variables after optimization.
  assert optimize()==[approximately_optimized_values]
output: def optimize():
      from scipy.optimize import minimize
      
      def obj_func(x):
          return (x[0] - 1)**2 + (x[1] - 2.5)**2
      
      x0 = [2, 0]
      result = minimize(obj_func, x0, method='nelder-mead', options={'xatol': 1e-8, 'disp': True})
      
      return result.x