code: def test_classification_head(self):
      if not hasattr(
          ADAPTER_MODEL_MAPPING[self.config_class],
          "add_classification_head",
      ):
          self.skipTest("No classification head")
  
      model1, model2 = create_twin_models(AutoAdapterModel, self.config)
  
      model1.add_classification_head("dummy")
      label_dict = {}
      label_dict["labels"] = torch.zeros(
          self.batch_size, dtype=torch.long, device=torch_device
      )
      self.run_prediction_head_test(
          model1, model2, "dummy", label_dict=label_dict
      )
  
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 2 for calling the PredictionHeadModelTestMixin obj's underlying member method code with example usage: obj.test_classification_head() without return types