input: Write a function to optimize a given multivariate function using the `scipy.optimize.minimize` method. The function should take a multivariate function and an initial guess as input, and return the optimized values of the variables. If the optimization fails, return None.
  
  assert optimize_function(lambda x: x[0]**2 + x[1]**2, [1, 1])==[0.0, 0.0]
  assert optimize_function(lambda x: (x[0] - 2)**2 + (x[1] + 3)**2, [0, 0])==[2.0, -3.0]
  assert optimize_function(lambda x: x[0] + x[1], [1, 1])==[-1.0125168, -0.1]
output: def optimize_function(func, initial_guess):
      from scipy.optimize import minimize
      result = minimize(func, initial_guess)
      if result.success:
          return result.x.tolist()
      else:
          return None