code: def test_sequential():
      class Masked(torch.nn.Module):
          def forward(self, x, m):
              return x, m
  
      from espnet.nets.pytorch_backend.transformer.repeat import (
          MultiSequential,
      )
  
      f = MultiSequential(Masked(), Masked())
      x = torch.randn(2, 3)
      m = torch.randn(2, 3) > 0
      assert len(f(x, m)) == 2
      if torch.cuda.is_available():
          f = torch.nn.DataParallel(f)
          f.cuda()
          assert len(f(x.cuda(), m.cuda())) == 2
  
file_path: espnet/test/test_e2e_asr_transformer.py
imports: import argparse
  import chainer
  import numpy
  import pytest
  import torch
  import espnet.nets.chainer_backend.e2e_asr_transformer
  import espnet.nets.pytorch_backend.e2e_asr_transformer
  from espnet.nets.pytorch_backend.nets_utils import rename_state_dict
  from espnet.nets.pytorch_backend.transformer import plot
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask, target_mask
label_desc: Use image node_id 1 for calling a global function with example usage: test_sequential() and returns: x,  m