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