code: def create_twin_adapters(self, model, name, adapter_config):
      # create adapter
      adapter1, adapter2 = name + "_1", name + "_2"
      model.add_adapter(adapter1, config=adapter_config)
      self.add_head(model, adapter1)
      # create a twin initialized with the same random weights
      model.add_adapter(adapter2, config=adapter_config)
      self.add_head(model, adapter2)
  
      state_dict = model.state_dict()
      for k, v in state_dict.items():
          if adapter1 in k:
              state_dict[k.replace(adapter1, adapter2)] = v
      model.load_state_dict(state_dict)
  
      return adapter1, adapter2
  
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 1 for calling the ParallelTrainingMixin obj's underlying member method code with example usage: obj.create_twin_adapters(model, name, adapter_config) and returns: adapter1,  adapter2