code: def test_gpu_trainable(module):
      m = importlib.import_module(module)
      args = make_arg()
      model = m.E2E(6, 5, args)
      if "pytorch" in module:
          batch = prepare_inputs("pytorch", is_cuda=True)
          model.cuda()
      else:
          raise NotImplementedError
      loss = model(*batch)
      if isinstance(loss, tuple):
          # chainer return several values as tuple
          loss[0].backward()  # trainable
      else:
          loss.backward()
  
imports: from __future__ import division
  import argparse
  import importlib
  import os
  import tempfile
  from test.utils_test import make_dummy_json_mt
  import chainer
  import numpy
  import pytest
  import torch
  from espnet.nets.pytorch_backend.nets_utils import pad_list
  from espnet.utils.training.batchfy import make_batchset
label_desc: Use image node_id 13 for calling a global function with example usage: test_gpu_trainable(module) without return types