input: Write a function to optimize a given multivariate function using the scipy.optimize module. The function should take a function and an initial guess as input and return the optimized values of the variables.
  assert list(optimize_function(lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2, [2, 0])) == [1.0, 2.5]
  assert list(optimize_function(lambda x: x[0]**2 + x[1]**2, [1, 1])) == [0.0, 0.0]
  assert list(optimize_function(lambda x: x[0]**4 - 16*x[0]**2 - 5*x[0] + x[1]**4 - 16*x[1]**2 - 5*x[1], [0, 0])) == [-2.90353403, -2.90353403]
  assert list(optimize_function(lambda x: (x[0] - 3)**2 + (x[1] + 2)**2, [0, 0])) == [3.0, -2.0]
  assert list(optimize_function(lambda x: x[0]**2 + x[1]**2 + x[2]**2, [1, 1, 1])) == [0.0, 0.0, 0.0]
output: def optimize_function(func, initial_guess):
      from scipy.optimize import minimize
      result = minimize(func, initial_guess)
      return result.x