code: def test_fastspeech_gpu_trainable_and_decodable(
      teacher_type, model_dict
  ):
      # make args
      idim, odim = 10, 25
      model_args = make_feedforward_transformer_args(**model_dict)
  
      # setup batch
      ilens = [10, 5]
      olens = [20, 15]
      device = torch.device("cuda")
      batch = prepare_inputs(
          idim,
          odim,
          ilens,
          olens,
          model_args["spk_embed_dim"],
          device=device,
      )
  
      # define teacher model and save it
      if teacher_type == "transformer":
          teacher_model_args = make_transformer_args(**model_dict)
          teacher_model = Transformer(
              idim, odim, Namespace(**teacher_model_args)
          )
      elif teacher_type == "tacotron2":
          teacher_model_args = make_taco2_args(**model_dict)
          teacher_model = Tacotron2(
              idim, odim, Namespace(**teacher_model_args)
          )
      else:
          raise ValueError()
      tmpdir = tempfile.mkdtemp(prefix="tmp_", dir="/tmp")
      torch.save(
          teacher_model.state_dict(), tmpdir + "/model.dummy.best"
      )
      with open(tmpdir + "/model.json", "wb") as f:
          f.write(
              json.dumps(
                  (idim, odim, teacher_model_args),
                  indent=4,
                  ensure_ascii=False,
                  sort_keys=True,
              ).encode("utf_8")
          )
  
      # define model
      model_args["teacher_model"] = tmpdir + "/model.dummy.best"
      model = FeedForwardTransformer(
          idim, odim, Namespace(**model_args)
      )
      model.to(device)
      optimizer = torch.optim.Adam(model.parameters())
  
      # trainable
      loss = model(**batch).mean()
      optimizer.zero_grad()
      loss.backward()
      optimizer.step()
  
      # 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]], None, spemb=spemb
          )
          model.calculate_all_attentions(**batch)
  
      # remove tmpdir
      if os.path.exists(tmpdir):
          shutil.rmtree(tmpdir)
  
imports: import json
  import os
  import shutil
  import tempfile
  from argparse import Namespace
  import numpy
  import pytest
  import torch
  from espnet.nets.pytorch_backend.e2e_tts_fastspeech import FeedForwardTransformer
  from espnet.nets.pytorch_backend.e2e_tts_tacotron2 import Tacotron2
  from espnet.nets.pytorch_backend.e2e_tts_transformer import Transformer
  from espnet.nets.pytorch_backend.fastspeech.duration_calculator import DurationCalculator
  from espnet.nets.pytorch_backend.fastspeech.length_regulator import LengthRegulator
  from espnet.nets.pytorch_backend.nets_utils import pad_list
label_desc: Use image node_id 6 for calling a global function with example usage: test_fastspeech_gpu_trainable_and_decodable(teacher_type, model_dict) without return types