code: def test_parallel_training_single_forward_pass(self):
      model = AutoAdapterModel.from_config(self.config())
      model.eval()
  
      a1, a2 = self.create_twin_adapters(model, "a", SeqBnConfig())
      b1, b2 = self.create_twin_adapters(model, "b", SeqBnConfig())
  
      state_dict = model.state_dict()
      for k, v in state_dict.items():
          if a1 in k:
              self.assertTrue(
                  torch.equal(v, state_dict[k.replace(a1, a2)])
              )
          if b1 in k:
              self.assertTrue(
                  torch.equal(v, state_dict[k.replace(b1, b2)])
              )
  
      input_data = self.get_input_samples(config=model.config)
      if isinstance(model, BertGenerationAdapterModel):
          input_data["labels"] = torch.randint(
              0, 2, (3, 64), device=torch_device
          )
      else:
          input_data["labels"] = torch.randint(
              0, 2, (3, 1), device=torch_device
          )
  
      outputs = []
      for adapter in [a1, b1]:
          model.active_head = adapter
          model.set_active_adapters(adapter)
          model.train_adapter(adapter)
          model.eval()
          model.to(torch_device)
          outputs.append(model(**input_data))
  
      model.set_active_adapters(Parallel(a2, b2))
      model.train_adapter((Parallel(a2, b2)))
      model.eval()
      model.to(torch_device)
  
      parallel_outputs = model(**input_data)
  
      for out1, out2 in zip(outputs, parallel_outputs.head_outputs):
          self.assertTrue(torch.allclose(out1["loss"], out2["loss"]))
          self.assertTrue(
              torch.allclose(out1["logits"], out2["logits"], atol=1e-5)
          )
  
imports: import copy
  import random
  import torch
  from adapters import ADAPTER_MODEL_MAPPING, AutoAdapterModel, PrefixTuningConfig, SeqBnConfig, T5AdapterModel
  from adapters.composition import BatchSplit, Parallel
  from adapters.models.bert_generation.adapter_model import BertGenerationAdapterModel
  from transformers import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, Trainer, TrainingArguments
  from transformers.testing_utils import require_torch, torch_device
label_desc: Use image node_id 9 for calling the ParallelTrainingMixin obj's underlying member method code with example usage: obj.test_parallel_training_single_forward_pass() without return types