input: Write a function to optimize the given objective function using the scipy.optimize library. The function should set up the optimization problem with a starting point of [0.0, 0.0, 0.0], use the Nelder-Mead method, and return the optimized parameters. 
  
  assert optimize()==[0.0, 0.0, 0.0]
  assert obj_func([0.0, 0.0, 0.0])==0.0
output: def optimize():
      from scipy.optimize import minimize
      def obj_func(x):
          return x[0]**2 + x[1]**2 + x[2]**2
      result = minimize(obj_func, [0.0, 0.0, 0.0], method='Nelder-Mead')
      return result.x