code: def run_parallel_training_equivalent_to_single(self, adapter_config):
      model = AutoAdapterModel.from_config(self.config())
      model.eval()
  
      a1, a2 = self.create_twin_adapters(model, "a", adapter_config)
      b1, b2 = self.create_twin_adapters(model, "b", adapter_config)
  
      dataset = []
      for i in range(3):
          input_data = self.get_input_samples(config=model.config)
          if isinstance(model, BertGenerationAdapterModel):
              input_data["labels"] = torch.randint(0, 2, (3, 64))
          else:
              input_data["labels"] = torch.randint(0, 2, (3, 1))
          dataset.append(input_data)
  
      for adapter in [a1, b1]:
          model.active_head = adapter
          model.set_active_adapters(adapter)
          model.train_adapter(adapter)
          model.eval()
  
          model = self.train_model(model, dataset)
  
      model.set_active_adapters(Parallel(a2, b2))
      model.train_adapter((Parallel(a2, b2)))
      model.eval()
  
      model = self.train_model(model, dataset)
  
      state_dict = model.state_dict()
      for k, v in state_dict.items():
          if a1 in k:
              self.assertTrue(
                  torch.allclose(
                      v, state_dict[k.replace(a1, a2)], atol=1e-5
                  ),
                  torch.max(
                      torch.sub(v, state_dict[k.replace(a1, a2)])
                  ),
              )
          if b1 in k:
              self.assertTrue(
                  torch.allclose(
                      v, state_dict[k.replace(b1, b2)], 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 4 for calling the ParallelTrainingMixin obj's underlying member method code with example usage: obj.run_parallel_training_equivalent_to_single(adapter_config) without return types