code: def test_multi_save(self):
      model = TheModelClass()
      optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
      torch.save(
          {
              "epoch": 5,
              "model_state_dict": model.state_dict(),
              "optimizer_state_dict": optimizer.state_dict(),
              "loss": 1.842,
          },
          "checkpoint.pt",
          encryption_key=encryption_key,
      )
      checkpoint = torch.load(
          "checkpoint.pt", decryption_key=encryption_key
      )
      self.assertEqual(checkpoint["epoch"], 5)
      self.assertEqual(checkpoint["loss"], 1.842)
      self.assertEqual(
          optimizer.state_dict(), checkpoint["optimizer_state_dict"]
      )
      for param_tensor in model.state_dict():
          self.assertTrue(
              torch.equal(
                  model.state_dict()[param_tensor],
                  checkpoint["model_state_dict"][param_tensor],
              )
          )
  
imports: import torch.nn.functional
  import torch, os, io
  import base64
  import torch.nn
  import unittest
  import random
  from bigdl.nano.pytorch.patching import patch_encryption
  import torch.optim
label_desc: Use image node_id 5 for calling the TestModelSaveLoad obj's underlying member method code with example usage: obj.test_multi_save() without return types