code: def train_model(self, model, dataset):
      # trains model in eval mode for 2 epochs
      random.seed(42)
      torch.manual_seed(42)
      # Depending on the used optimizer the adapters are not exactly the same
      model.to(torch_device)
      optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
      for epoch in range(2):
          for data_input in dataset:
              for key, value in data_input.items():
                  data_input[key] = value.to(torch_device)
  
              optimizer.zero_grad()
              output = model(**data_input)
              loss = output["loss"]
              loss.backward()
              optimizer.step()
      return model
  
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 2 for calling the ParallelTrainingMixin obj's underlying member method code with example usage: obj.train_model(model, dataset) and returns: model