code: def test_pytorch_multi_gpu_trainable(train_dic):
      idim, odim, ilens, olens = get_default_scope_inputs()
      train_args = get_default_train_args(**train_dic)
  
      ngpu = 2
      device_ids = list(range(ngpu))
  
      model = E2E(idim, odim, train_args)
      model = torch.nn.DataParallel(model, device_ids)
      model.cuda()
  
      batch = prepare_inputs(idim, odim, ilens, olens, is_cuda=True)
  
      loss = 1.0 / ngpu * model(*batch)
      loss.backward(loss.new_ones(ngpu))
  
imports: import argparse
  import json
  import tempfile
  import numpy
  import pytest
  import torch
  from packaging.version import parse
  import espnet.lm.pytorch_backend.extlm
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.pytorch_backend.asr_init import load_trained_model
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.e2e_asr_transducer import E2E
  from espnet.nets.pytorch_backend.nets_utils import pad_list
label_desc: Use image node_id 8 for calling a global function with example usage: test_pytorch_multi_gpu_trainable(train_dic) without return types