input: Write a function to optimize the given objective function using the scipy.optimize.minimize method. The function should accept an initial guess and return the optimized parameters.
  
  from scipy.optimize import minimize 
  
  def obj_func(x):
      # objective function
      return (x[0] - 1)**2 + (x[1] - 2.5)**2 + (x[2] - 3)**2
  
  def optimize(x0):
      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]))==[1.0, 2.5, 3.0]
  assert list(optimize([-1.0, 3.0, 5.0]))==[1.0, 2.5, 3.0]
  assert list(optimize([1.0, 2.5, 3.0]))==[1.0, 2.5, 3.0]
output: def optimize(x0):
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x