code: def test_invertible_adapter_forward(self):
      model = self.get_model()
      model.eval()
  
      for adapter_config, _ in self.inv_adapter_configs_to_test:
          with self.subTest(
              model_class=model.__class__.__name__,
              config=adapter_config.__class__.__name__,
          ):
              self.run_forward_test(model, adapter_config)
  
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 11 for calling the BottleneckAdapterTestMixin obj's underlying member method code with example usage: obj.test_invertible_adapter_forward() without return types