code: def test_reference_embedding(self):
      model = AutoAdapterModel.from_config(
          self.config()
      )  # self.get_model()
      tokenizer = AutoTokenizer.from_pretrained(
          self.tokenizer_name, use_fast=False
      )
      if tokenizer.pad_token is None:
          tokenizer.pad_token = tokenizer.eos_token
      new_tokenizer = AutoTokenizer.from_pretrained(
          "tests/fixtures/SiBERT"
      )
  
      model.add_embeddings("test", new_tokenizer, "default", tokenizer)
  
      default_embedding = model.base_model.loaded_embeddings["default"]
      test_embedding = model.base_model.loaded_embeddings["test"]
  
      input_test = []
      input_default = []
  
      for v, idx in new_tokenizer.get_vocab().items():
          if v in tokenizer.get_vocab() and not v.startswith("##"):
              input_test.append(idx)
              input_default.append(tokenizer.get_vocab()[v])
              if len(input_test) >= 5:
                  break
  
      input_default = torch.tensor([input_default])
      input_test = torch.tensor([input_test])
  
      default = default_embedding(input_default)
      test = test_embedding(input_test)
  
      self.assertTrue(torch.equal(default, test))
  
      # activate for training
      model.add_adapter("test")
      model.train_adapter("test", train_embeddings=True)
  
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 8 for calling the EmbeddingTestMixin obj's underlying member method code with example usage: obj.test_reference_embedding() without return types