code: def test_loading_adapter_weights_without_prefix(self):
      if self.config_class not in ADAPTER_MODEL_MAPPING:
          self.skipTest("Does not support flex heads.")
  
      model_base, model_with_head = create_twin_models(
          self.model_class, self.config
      )
      model_base = model_base.base_model  # use base model w/o prefix
  
      model_base.add_adapter("dummy")
  
      with tempfile.TemporaryDirectory() as temp_dir:
          model_base.save_adapter(temp_dir, "dummy")
  
          loading_info = {}
          model_with_head.load_adapter(
              temp_dir, loading_info=loading_info
          )
  
      self.assertEqual(0, len(loading_info["missing_keys"]))
      self.assertEqual(0, len(loading_info["unexpected_keys"]))
  
      # check equal output
      input_data = self.get_input_samples(config=model_with_head.config)
      model_with_head.to(torch_device)
      model_base.to(torch_device)
      output1 = model_with_head(**input_data)
      output2 = model_base(**input_data)
      self.assertEqual(len(output1), len(output2))
      self.assertTrue(torch.equal(output1[0], output2[0]))
  
imports: import copy
  import tempfile
  import torch
  import adapters
  from adapters import ADAPTER_CONFIG_MAP, ADAPTER_MODEL_MAPPING, AutoAdapterModel, BatchSplit, DoubleSeqBnConfig, DoubleSeqBnInvConfig, Fuse, InvertibleAdaptersMixin, InvertibleAdaptersWrapperMixin, MAMConfig, SeqBnConfig, SeqBnInvConfig
  from adapters.heads.language_modeling import CausalLMHead
  from transformers import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, CLIPConfig
  from transformers.testing_utils import require_torch, torch_device
  from .base import AdapterMethodBaseTestMixin, create_twin_models
label_desc: Use image node_id 18 for calling the BottleneckAdapterTestMixin obj's underlying member method code with example usage: obj.test_loading_adapter_weights_without_prefix() without return types