code: def test_transformer_trainable_and_decodable(model_dict):
      args = make_arg(**model_dict)
      model, x, ilens, y, data, uttid_list = prepare(args)
  
      # decoding params
      recog_args = argparse.Namespace(
          maskctc_n_iterations=args.maskctc_n_iterations,
          maskctc_probability_threshold=args.maskctc_probability_threshold,
      )
      # test training
      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 decoding
      with torch.no_grad():
          model.recognize(
              x[0, : ilens[0]].numpy(), recog_args, args.char_list
          )
  
file_path: espnet/test/test_e2e_asr_maskctc.py
imports: import argparse
  import pytest
  import torch
  from espnet.nets.pytorch_backend.e2e_asr_maskctc import E2E
  from espnet.nets.pytorch_backend.maskctc.add_mask_token import mask_uniform
  from espnet.nets.pytorch_backend.transformer import plot
label_desc: Use image node_id 5 for calling a global function with example usage: test_transformer_trainable_and_decodable(model_dict) without return types