code: def test_fastspeech_inference(alpha):
      # make args
      idim, odim = 10, 25
      model_args = make_feedforward_transformer_args()
  
      # setup batch
      ilens = [10, 5]
      olens = [20, 15]
      batch = prepare_inputs(
          idim, odim, ilens, olens, model_args["spk_embed_dim"]
      )
  
      # define model
      model = FeedForwardTransformer(
          idim, odim, Namespace(**model_args)
      )
  
      # test inference
      inference_args = Namespace(**{"fastspeech_alpha": alpha})
      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]],
              inference_args,
              spemb=spemb,
          )
  
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 11 for calling a global function with example usage: test_fastspeech_inference(alpha) without return types