code: def batched_training_pass(self, model):
      inputs = {
          "input_ids": ids_tensor((4, 128), 1000).to(torch_device),
          "labels": torch.ones(4, 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 4 for calling the AdapterCompositionTest obj's underlying member method code with example usage: obj.batched_training_pass(model) without return types