input: Write a function to optimize the given objective function using the Nelder-Mead method from the `scipy.optimize` library. The objective function should be minimized starting from an initial guess `x0 = [0.0, 0.0, 0.0]`. The function should return the optimized values of the variables.
  
  assert obj_func([1.0, 1.0, 1.0])==some_value
  assert obj_func([0.5, 0.5, 0.5])==another_value
  assert obj_func([2.0, 2.0, 2.0])==yet_another_value
  assert obj_func([0.0, 0.0, 0.0])==yet_another_value
  assert obj_func([-1.0, -1.0, -1.0])==yet_another_value
  print(optimize())==[optimized_values]
output: 
  def optimize():
      from scipy.optimize import minimize
      x0 = [0.0, 0.0, 0.0]
      result = minimize(obj_func, x0, method='Nelder-Mead')
      return result.x
  