code: def test_save_load_embedding(self):
      model = self.get_model()
      tokenizer = AutoTokenizer.from_pretrained("tests/fixtures/SiBERT")
      input_data = self.get_input_samples(
          (1, 128), vocab_size=tokenizer.vocab_size, config=model.config
      )
      model.add_embeddings("test", tokenizer)
      model.eval()
      model.to(torch_device)
      output1 = model(**input_data)
      self.assertEqual(model.active_embeddings, "test")
  
      with tempfile.TemporaryDirectory() as tmp_dir:
          model.save_embeddings(tmp_dir, "test", tokenizer=tokenizer)
          tokenizer_ref = model.load_embeddings(
              tmp_dir, "test_reloaded"
          )
  
      self.assertEqual(model.active_embeddings, "test_reloaded")
      model.to(torch_device)
      output2 = model(**input_data)
      self.assertTrue(
          torch.equal(
              model.loaded_embeddings["test"].weight,
              model.loaded_embeddings["test_reloaded"].weight,
          )
      )
      self.assertTrue(torch.equal(output1[0], output2[0]))
      self.assertEqual(tokenizer.vocab, tokenizer_ref.vocab)
  
file_path: adapters/tests/test_adapter_embeddings.py
imports: import copy
  import tempfile
  import torch
  from adapters import AutoAdapterModel
  from transformers import AutoTokenizer, Trainer, TrainingArguments
  from transformers.testing_utils import require_torch, torch_device
label_desc: Use image node_id 5 for calling the EmbeddingTestMixin obj's underlying member method code with example usage: obj.test_save_load_embedding() without return types