code: def test_parallel_wavegan_compatibility():
      from parallel_wavegan.models import (
          StyleMelGANGenerator as PWGStyleMelGANGenerator,
      )
  
      model_pwg = PWGStyleMelGANGenerator(
          **make_style_melgan_generator_args()
      )
      model_espnet2 = StyleMelGANGenerator(
          **make_style_melgan_generator_args()
      )
      model_espnet2.load_state_dict(model_pwg.state_dict())
      model_pwg.eval()
      model_espnet2.eval()
  
      with torch.no_grad():
          c = torch.randn(3, 5)
          torch.manual_seed(1)
          out_pwg = model_pwg.inference(c)
          torch.manual_seed(1)
          out_espnet2 = model_espnet2.inference(c)
          np.testing.assert_array_equal(
              out_pwg.cpu().numpy(),
              out_espnet2.cpu().numpy(),
          )
  
file_path: espnet/test/espnet2/gan_tts/style_melgan/test_style_melgan.py
imports: import numpy
  import pytest
  import torch
  from espnet2.gan_tts.hifigan.loss import DiscriminatorAdversarialLoss, GeneratorAdversarialLoss
  from espnet2.gan_tts.style_melgan import StyleMelGANDiscriminator, StyleMelGANGenerator
label_desc: Use image node_id 4 for calling a global function with example usage: test_parallel_wavegan_compatibility() without return types