code: def test_segment_streaming_e2e():
      args = make_arg()
      args.etype = "vgglstm"  # uni-directional
      args.batchsize = 0
      model = th_asr.E2E(10, 5, args)
      asr = SegmentStreamingE2E(model, args)
  
      in_data = np.random.randn(50, 10)
      r = np.prod(model.subsample)
      for i in range(0, 50, r):
          asr.accept_input(in_data[i : i + r])
  
      args.batchsize = 1
      for i in range(0, 50, r):
          asr.accept_input(in_data[i : i + r])
  
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 6 for calling a global function with example usage: test_segment_streaming_e2e() without return types