code: def test_transformer_trainable_and_decodable(model_dict):
      # make args
      model_args = make_transformer_args(**model_dict)
      inference_args = make_inference_args()
  
      # setup batch
      idim = 5
      odim = 10
      ilens = [10, 5]
      olens = [20, 15]
      batch = prepare_inputs(
          idim, odim, ilens, olens, model_args["spk_embed_dim"]
      )
  
      # define model
      model = Transformer(idim, odim, Namespace(**model_args))
      optimizer = torch.optim.Adam(model.parameters())
  
      # trainable
      loss = model(**batch).mean()
      optimizer.zero_grad()
      loss.backward()
      optimizer.step()
  
      # check gradient of ScaledPositionalEncoding
      if model.use_scaled_pos_enc:
          assert model.encoder.embed[1].alpha.grad is not None
          assert model.decoder.embed[1].alpha.grad is not None
  
      # decodable
      model.eval()
      with torch.no_grad():
          if model_args["spk_embed_dim"] is None:
              spemb = None
          else:
              spemb = batch["spembs"][0]
          model.inference(
              batch["xs"][0][: batch["ilens"][0]],
              Namespace(**inference_args),
              spemb=spemb,
          )
          model.calculate_all_attentions(**batch)
  
file_path: espnet/test/test_e2e_tts_transformer.py
imports: from argparse import Namespace
  import numpy
  import pytest
  import torch
  from espnet.nets.pytorch_backend.e2e_tts_transformer import Transformer, subsequent_mask
  from espnet.nets.pytorch_backend.nets_utils import pad_list
label_desc: Use image node_id 4 for calling a global function with example usage: test_transformer_trainable_and_decodable(model_dict) without return types