code: def test_transformer_trainable_and_decodable(module, model_dict):
      args = make_arg(**model_dict)
      model, x, ilens, y, data, uttid_list = prepare(module, args)
  
      # check for pure CTC and pure Attention
      if args.mtlalpha == 1:
          assert model.decoder is None
      elif args.mtlalpha == 0:
          assert model.ctc is None
  
      # test beam search
      recog_args = argparse.Namespace(
          beam_size=1,
          penalty=0.0,
          ctc_weight=0.0,
          maxlenratio=1.0,
          lm_weight=0,
          minlenratio=0,
          nbest=1,
      )
      if module == "pytorch":
          # test trainable
          optim = torch.optim.Adam(model.parameters(), 0.01)
          loss = model(x, ilens, y)
          optim.zero_grad()
          loss.backward()
          optim.step()
  
          # test attention plot
          attn_dict = model.calculate_all_attentions(
              x[0:1], ilens[0:1], y[0:1]
          )
          plot.plot_multi_head_attention(
              data, uttid_list, attn_dict, "", savefn=_savefn
          )
  
          # test CTC plot
          ctc_probs = model.calculate_all_ctc_probs(
              x[0:1], ilens[0:1], y[0:1]
          )
          if args.mtlalpha > 0:
              print(ctc_probs.shape)
          else:
              assert ctc_probs is None
  
          # test decodable
          with torch.no_grad():
              nbest = model.recognize(
                  x[0, : ilens[0]].numpy(), recog_args
              )
              print(y[0])
              print(nbest[0]["yseq"][1:-1])
      else:
          # test trainable
          optim = chainer.optimizers.Adam(0.01)
          optim.setup(model)
          loss, loss_ctc, loss_att, acc = model(x, ilens, y)
          model.cleargrads()
          loss.backward()
          optim.update()
  
          # test attention plot
          attn_dict = model.calculate_all_attentions(
              x[0:1], ilens[0:1], y[0:1]
          )
          plot.plot_multi_head_attention(
              data, uttid_list, attn_dict, "", savefn=_savefn
          )
  
          # test decodable
          with chainer.no_backprop_mode():
              nbest = model.recognize(x[0, : ilens[0]], recog_args)
              print(y[0])
              print(nbest[0]["yseq"][1:-1])
  
file_path: espnet/test/test_e2e_asr_transformer.py
imports: import argparse
  import chainer
  import numpy
  import pytest
  import torch
  import espnet.nets.chainer_backend.e2e_asr_transformer
  import espnet.nets.pytorch_backend.e2e_asr_transformer
  from espnet.nets.pytorch_backend.nets_utils import rename_state_dict
  from espnet.nets.pytorch_backend.transformer import plot
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask, target_mask
label_desc: Use image node_id 8 for calling a global function with example usage: test_transformer_trainable_and_decodable(module, model_dict) without return types