code: def test_tacotron2_gpu_trainable_and_decodable(
      model_dict, inference_dict
  ):
      bs = 2
      maxin_len = 10
      maxout_len = 10
      idim = 5
      odim = 10
      device = torch.device("cuda")
      model_args = make_taco2_args(**model_dict)
      inference_args = make_inference_args(**inference_dict)
      batch = prepare_inputs(
          bs,
          idim,
          odim,
          maxin_len,
          maxout_len,
          model_args["spk_embed_dim"],
          model_args["spc_dim"],
          device=device,
      )
  
      # define model
      model = Tacotron2(idim, odim, Namespace(**model_args))
      optimizer = torch.optim.Adam(model.parameters())
      model.to(device)
  
      # trainable
      loss = model(**batch).mean()
      optimizer.zero_grad()
      loss.backward()
      optimizer.step()
  
      # decodable
      model.eval()
      with torch.no_grad():
          spemb = (
              None
              if model_args["spk_embed_dim"] is None
              else batch["spembs"][0]
          )
          model.inference(
              batch["xs"][0][: batch["ilens"][0]],
              Namespace(**inference_args),
              spemb,
          )
          model.calculate_all_attentions(**batch)
  
file_path: espnet/test/test_e2e_vc_tacotron2.py
imports: from __future__ import division, print_function
  from argparse import Namespace
  import numpy
  import pytest
  import torch
  from espnet.nets.pytorch_backend.e2e_vc_tacotron2 import Tacotron2
  from espnet.nets.pytorch_backend.nets_utils import pad_list
label_desc: Use image node_id 5 for calling a global function with example usage: test_tacotron2_gpu_trainable_and_decodable(model_dict, inference_dict) without return types