code: def test_calculate_all_attentions(module, atype):
      m = importlib.import_module(module)
      args = make_arg(atype=atype)
      if "pytorch" in module:
          batch = prepare_inputs("pytorch")
      else:
          batch = prepare_inputs("chainer")
      model = m.E2E(10, 5, args)
      with chainer.no_backprop_mode():
          if "pytorch" in module:
              att_ws = model.calculate_all_attentions(*batch)[0]
          else:
              att_ws = model.calculate_all_attentions(*batch)
          print(att_ws.shape)
  
imports: from __future__ import division
  import argparse
  import importlib
  import os
  import tempfile
  from test.utils_test import make_dummy_json
  import chainer
  import numpy
  import pytest
  import torch
  import espnet.nets.chainer_backend.e2e_asr
  import espnet.nets.pytorch_backend.e2e_asr
  from espnet.asr import asr_utils
  from espnet.nets.pytorch_backend.nets_utils import pad_list
  from espnet.nets.pytorch_backend.streaming.segment import SegmentStreamingE2E
  from espnet.nets.pytorch_backend.streaming.window import WindowStreamingE2E
  from espnet.utils.training.batchfy import make_batchset
label_desc: Use image node_id 16 for calling a global function with example usage: test_calculate_all_attentions(module, atype) without return types