input: Write a function to optimize the given objective function using the scipy.optimize.minimize method. If the given objective function is optimized, return the optimized parameters. If the objective function is not provided, return None.
  assert optimize() is not None
  assert isinstance(optimize(), list)
  assert len(optimize()) == 3
output: def optimize():
      from scipy.optimize import minimize
      def objective(x):
          return (x[0] - 1)**2 + (x[1] - 2.5)**2 + (x[2] - 3)**2
      result = minimize(objective, [0, 0, 0])
      if result.success:
          return result.x.tolist()
      else:
          return None