def METHOD_NAME():
    from parallel_wavegan.models import (
        ParallelWaveGANGenerator as PWGParallelWaveGANGenerator,
    )
    model_pwg = PWGParallelWaveGANGenerator(**make_generator_args())
    model_espnet2 = ParallelWaveGANGenerator(**make_generator_args())
    model_espnet2.load_state_dict(model_pwg.state_dict())
    model_pwg.eval()
    model_espnet2.eval()
    # NOTE(kan-bayashi): Use float64 to avoid numerical error in CI
    dtype = torch.float64
    model_pwg.to(dtype=dtype)
    model_espnet2.to(dtype=dtype)
    with torch.no_grad():
        z = torch.randn(3 * 16, 1, dtype=dtype)
        c = torch.randn(3, 10, dtype=dtype)
        out_pwg = model_pwg.inference(c, z)
        out_espnet2 = model_espnet2.inference(c, z)
        np.testing.assert_allclose(
            out_pwg.cpu().numpy(),
            out_espnet2.cpu().numpy(),
            rtol=1e-5,
        )