code: def test_reload_static_to_flex_head(self):
      if not hasattr(
          ADAPTER_MODEL_MAPPING[self.config_class],
          "add_classification_head",
      ):
          self.skipTest("No classification head available")
      static_head_model = (
          AutoModelForSequenceClassification.from_config(self.config())
      )
      adapters.init(static_head_model)
      flex_head_model = AutoAdapterModel.from_pretrained(
          None,
          config=self.config(),
          state_dict=static_head_model.state_dict(),
      )
      static_head_model.eval()
      flex_head_model.eval()
  
      static_head_model.add_adapter("test")
  
      with tempfile.TemporaryDirectory() as temp_dir:
          static_head_model.save_adapter(temp_dir, "test")
  
          loading_info = {}
          flex_head_model.load_adapter(
              temp_dir, loading_info=loading_info
          )
  
          # Load the adapter a second time to make sure our conversion script doesn't break anything
          flex_head_model.load_adapter(
              temp_dir, loading_info=loading_info
          )
      self.assertEqual(0, len(loading_info["missing_keys"]))
      self.assertEqual(0, len(loading_info["unexpected_keys"]))
  
      # adapter and head were loaded
      self.assertIn("test", flex_head_model.adapters_config)
      self.assertIn("test", flex_head_model.heads)
  
      # check equal output
      in_data = self.get_input_samples(config=flex_head_model.config)
      static_head_model.to(torch_device)
      flex_head_model.to(torch_device)
      with AdapterSetup("test"):
          output1 = static_head_model(**in_data)
          output2 = flex_head_model(**in_data, head="test")
      self.assertTrue(
          torch.all(torch.isclose(output1.logits, output2.logits))
      )
  
imports: import os
  import tempfile
  import torch
  import adapters
  from adapters import ADAPTER_MODEL_MAPPING, AdapterSetup, AutoAdapterModel
  from adapters.composition import BatchSplit, Stack
  from transformers import AutoModelForSequenceClassification
  from transformers.testing_utils import require_torch, torch_device
  from .methods import create_twin_models
label_desc: Use image node_id 18 for calling the PredictionHeadModelTestMixin obj's underlying member method code with example usage: obj.test_reload_static_to_flex_head() without return types