code: def prepare(E2E, args, mtlalpha=0.0):
      args.mtlalpha = mtlalpha
      args.char_list = ["a", "e", "i", "o", "u"]
      idim = 8
      odim = len(args.char_list)
      model = dynamic_import_asr(E2E, "pytorch")(idim, odim, args)
  
      batchsize = 1
      x = torch.randn(batchsize, 20, idim)
      ilens = [20, 15]
      n_token = odim - 1
      # avoid 0 for eps in ctc
      y = (
          torch.rand(batchsize, 10) * n_token % (n_token - 1)
      ).long() + 1
      olens = [10, 2]
      for i in range(batchsize):
          x[i, ilens[i] :] = -1
          y[i, olens[i] :] = -1
  
      data = []
      for i in range(batchsize):
          data.append(
              (
                  "utt%d" % i,
                  {
                      "input": [{"shape": [ilens[i], idim]}],
                      "output": [{"shape": [olens[i]]}],
                  },
              )
          )
      return model, x, torch.tensor(ilens), y, data, args
  
file_path: espnet/test/test_beam_search_timesync_streaming.py
imports: from argparse import Namespace
  import pytest
  import torch
  from espnet.nets.asr_interface import dynamic_import_asr
  from espnet.nets.beam_search_timesync_streaming import BeamSearchTimeSyncStreaming
  from espnet.nets.lm_interface import dynamic_import_lm
  from espnet.nets.scorers.length_bonus import LengthBonus
label_desc: Use image node_id 1 for calling a global function with example usage: prepare(E2E, args, mtlalpha) and returns: model,  x,  torch,  y,  data,  args