def METHOD_NAME(self):
    from paddlenlp.transformers import T5Model
    model = T5Model.from_pretrained("t5-small", dtype="float32")
    model.eval()
    loss = model(
        input_ids=paddle.arange(100, 110, dtype="int64").reshape([1, -1]),
        decoder_input_ids=paddle.arange(100, 105, dtype="int64").reshape([1, -1]),
        return_dict=True,
    )
    ret = loss.last_hidden_state.abs().mean().item()
    np.testing.assert_allclose(ret, 0.1365441381931305, rtol=1e-7)
    # # dy2static
    # input_spec = [
    #     paddle.static.InputSpec(shape=[None, None], dtype="int64"),  # input_ids
    #     paddle.static.InputSpec(shape=[None, 2, None], dtype="int64"),  # pos_ids
    #     paddle.static.InputSpec(shape=[None, None, None, None], dtype="int64"),  # attn_ids
    # ]
    # with tempfile.TemporaryDirectory() as tempdir:
    #     paddlenlp.transformers.export_model(
    #         model=model,
    #         input_spec=input_spec,
    #         path=tempdir,
    #     )