input: Write a function to optimize a given multivariate function using the `scipy.optimize` library. The function should take a multivariate function and an initial guess as input, 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
  
  # Test cases
  assert optimize_function(lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2, [2, 0]) == [1.0, 2.5]
  assert optimize_function(lambda x: x[0]**2 + x[1]**2, [3, 4]) == [0.0, 0.0]
  assert optimize_function(lambda x: (x[0] - 2)**4 + (x[1] - 3)**2, [0, 0]) == [2.0, 3.0]
  
output: import scipy.optimize as opt 
    
  def optimize_function(function, initial_guess): 
      result = opt.minimize(function, initial_guess) 
      return result.x