input: Write a function to optimize a given multivariate function using the `scipy.optimize.minimize` method. The function should take the function to be optimized and an initial guess as inputs and return the optimized values of the variables. 
  
  
  import scipy.optimize as opt 
    
  def optimize_function(function, initial_guess): 
      result = opt.minimize(function, initial_guess) 
      return result.x
  
  # Example test cases:
  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] - 3)**2 + (x[1] + 4)**2, [-1, 2])) == [3.0, -4.0]
  assert list(optimize_function(lambda x: x[0]**2 + x[1]**3, [2, -1])) == [0.0, 0.0]
  assert list(optimize_function(lambda x: x[0]**4 + x[1]**4, [0, 0])) == [0.0, 0.0]
  
output: 
  def optimize_function(function, initial_guess): 
      result = opt.minimize(function, initial_guess) 
      return result.x
  