input: Write a function to optimize a given multivariate function using the `scipy.optimize.minimize` method. If the given multivariate function and initial guess are provided, return the optimal values of the variables as a list.
  assert optimize_function(lambda x: (x[0]-1)**2 + (x[1]-2)**2, [0, 0]) == [1.0, 2.0]
  assert optimize_function(lambda x: x[0]**2 + x[1]**2, [1, 1]) == [0.0, 0.0]
  assert optimize_function(lambda x: (x[0]-3)**2 + (x[1]+5)**2, [2, -4]) == [3.0, -5.0]
  assert optimize_function(lambda x: x[0]**2 - x[1]**2, [0, 1]) == [0.0, 1.0]
  assert optimize_function(lambda x: (x[0]-5)**2 + (x[1]-5)**2, [4, 4]) == [5.0, 5.0]
output: 
  import scipy.optimize as opt
  
  def optimize_function(func, initial_guess):
      result = opt.minimize(func, initial_guess)
      return result.x.tolist()
  