input: Write a function to optimize the given objective function using the `scipy.optimize.minimize` method. The given objective function accepts an array `x` and should be implemented within the function `obj_func`. The `optimize` function should set up the optimization problem by providing an initial guess `x0`, and using the `nelder-mead` method with specified options. The function should return the optimized values of `x`.
  assert optimize() == [optimized_values] # Replace [optimized_values] with the actual expected optimized values from the objective function.
  
  # Example expected output
  # assert optimize() == [1.234, 2.345, 3.456]
output: def optimize():
      from scipy.optimize import minimize
  
      def obj_func(x):
          return (x[0] - 1)**2 + (x[1] - 2.5)**2 + (x[2] - 3.5)**2
  
      x0 = [0, 0, 0]
      result = minimize(obj_func, x0, method='nelder-mead', options={'xatol': 1e-8, 'disp': False})
      return result.x