input: Write a function to optimize a given multivariate function using the scipy.optimize module. The function should take in a function and an initial guess for the variables, and return the optimized values of the variables as obtained from the optimize method of the scipy.optimize module. If the given function or initial guess is invalid, return an appropriate error message.
  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, 0])) == [1.0, 2.5]
  assert list(optimize_function(lambda x: x[0]**2 + x[1]**2, [3, 4])) == [0.0, 0.0]
  assert list(optimize_function(lambda x: x[0] - x[1], [1, 1])) == [0.0, 0.0]
  assert list(optimize_function(lambda x: (x[0]**2 + x[1]**2)**0.5, [0, 0])) == [0.0, 0.0]
  assert list(optimize_function(lambda x: (x[0]**3 + x[1]**3), [-1, -1])) == [-1.0, -1.0]
output: def optimize_function(function, initial_guess): 
      try:
          result = opt.minimize(function, initial_guess) 
          return result.x
      except Exception as e:
          return str(e)