input: Write a function to optimize the given objective function using the scipy.optimize.minimize method. The function should return the optimal values of the variables. If the input is invalid or the optimization fails, it should return `None`.
  
  assert optimize() != None
  assert isinstance(optimize(), list)
  assert len(optimize()) == 3
  assert all(isinstance(x, float) for x in optimize())
output: def optimize():
      try:
          from scipy.optimize import minimize
          def objective(x):
              return (x[0] - 1)**2 + (x[1] - 2.5)**2 + (x[2] - 3.5)**2
          result = minimize(objective, [0, 0, 0])
          if result.success:
              return result.x.tolist()
          else:
              return None
      except:
          return None