input: Write a function to optimize a given multivariate function. The function should use the scipy.optimize module to minimize the given function and return the optimized values of the variables.
  
  def optimize_function(function, initial_guess): 
      result = opt.minimize(function, initial_guess) 
      return result.x
  
  assert tuple(optimize_function(lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2, [2, 0]))==(1.0000000004634265, 2.4999999994398245)
  assert tuple(optimize_function(lambda x: x[0]**2 + x[1]**2, [3, 4]))==(2.2825543996180458e-09, 3.0434058661573956e-09)
  assert tuple(optimize_function(lambda x: (x[0] - 5)**2 + (x[1] + 3)**2, [0, 0]))==(4.99999997528688, -2.9999999853797619)
  assert optimize_function(lambda x: x**2, [5])==[-4.74288014507709e-09]
  assert optimize_function(lambda x: x**2, [])==[]
output: 
  def optimize_function(function, initial_guess): 
      if not initial_guess:
          return []
      result = opt.minimize(function, initial_guess) 
      return result.x
  