input: Write a function to optimize the given objective function using the scipy.optimize.minimize method with the Nelder-Mead algorithm. The function should take an initial guess for the optimization variables and return the optimized values.
  from scipy.optimize import minimize 
  
  def obj_func(x):
      # objective function
      return x[0]**2 + x[1]**2 + x[2]**2
  
  def optimize(): 
      # set up optimization problem
      x0 = [0.0, 0.0, 0.0] 
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
      return res.x
  
  assert list(optimize()) == [0.0, 0.0, 0.0]
  assert round(optimize()[0], 1) == 0.0
  assert round(optimize()[1], 1) == 0.0
  assert round(optimize()[2], 1) == 0.0
output: def optimize(x0): 
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
      return res.x