code: def test_downloaded_asr_model_decodable(module, download_info):
      # download model
      print(download_info[0])
      tmpdir = tempfile.mkdtemp(prefix="tmp_", dir=".")
      model_path = download_zip_from_google_drive(
          tmpdir, download_info[1]
      )
  
      # load trained model parameters
      m = importlib.import_module(module)
      idim, odim, train_args = get_model_conf(model_path)
      model = m.E2E(idim, odim, train_args)
      if "chainer" in module:
          chainer_load(model_path, model)
      else:
          torch_load(model_path, model)
  
      with torch.no_grad(), chainer.no_backprop_mode():
          in_data = np.random.randn(128, idim)
          model.recognize(
              in_data, train_args, train_args.char_list
          )  # decodable
  
      # remove
      if os.path.exists(tmpdir):
          shutil.rmtree(tmpdir)
  
file_path: espnet/test/test_e2e_compatibility.py
imports: from __future__ import print_function
  import importlib
  import os
  import re
  import shutil
  import subprocess
  import tempfile
  from os.path import join
  import chainer
  import numpy
  import pytest
  import torch
  from espnet.asr.asr_utils import chainer_load, get_model_conf, torch_load
label_desc: Use image node_id 2 for calling a global function with example usage: test_downloaded_asr_model_decodable(module, download_info) without return types