code: def test_model_trainable_and_decodable(module, num_encs, model_dict):
      args = make_arg(num_encs=num_encs, **model_dict)
      batch = prepare_inputs("pytorch", num_encs)
  
      # test trainable
      m = importlib.import_module(module)
      model = m.E2E([2 for _ in range(num_encs)], 2, args)
      loss = model(*batch)
      loss.backward()  # trainable
  
      # test decodable
      with torch.no_grad():
          in_data = [np.random.randn(2, 2) for _ in range(num_encs)]
          model.recognize(in_data, args, args.char_list)  # decodable
          if "pytorch" in module:
              batch_in_data = [
                  [np.random.randn(5, 2), np.random.randn(2, 2)]
                  for _ in range(num_encs)
              ]
              model.recognize_batch(batch_in_data, args, args.char_list)
  
imports: from __future__ import division
  import argparse
  import importlib
  import os
  import tempfile
  from test.utils_test import make_dummy_json
  import numpy
  import pytest
  import torch
  from espnet.nets.pytorch_backend.nets_utils import pad_list
  from espnet.utils.training.batchfy import make_batchset
label_desc: Use image node_id 4 for calling a global function with example usage: test_model_trainable_and_decodable(module, num_encs, model_dict) without return types