code: def training_pass(self, model):
      inputs = {}
      inputs["input_ids"] = ids_tensor((1, 128), 1000).to(torch_device)
      inputs["labels"] = torch.ones(1, dtype=torch.long).to(
          torch_device
      )
      loss = model(**inputs).loss
      loss.backward()
  
imports: import unittest
  import torch
  import adapters
  from adapters import IA3Config, LoRAConfig, PrefixTuningConfig, SeqBnConfig
  from adapters.composition import Average, BatchSplit, Fuse, Parallel, Split, Stack, parse_composition
  from tests.test_adapter import ids_tensor
  from transformers import BertConfig, BertForSequenceClassification
  from transformers.testing_utils import require_torch, torch_device
label_desc: Use image node_id 3 for calling the AdapterCompositionTest obj's underlying member method code with example usage: obj.training_pass(model) without return types