input: Write a function to optimize the given objective function using the SciPy library. The function should initialize the optimization process with a starting point and return the optimal values of the variables.
  
  assert optimize() 
  assert len(optimize()) == 3
  assert isinstance(optimize()[0], float)
  assert isinstance(optimize()[1], float)
  assert isinstance(optimize()[2], float)
  
  You can copy and paste this function into your IDE for debugging or accepting URLs after solving automatically.
output: def optimize():
      from scipy.optimize import minimize
      def objective(x):
          return (x[0] - 1)**2 + (x[1] - 2.5)**2 + (x[2] - 3.5)**2
      
      initial_guess = [0, 0, 0]
      result = minimize(objective, initial_guess)
      
      return result.x