def METHOD_NAME(self, tmpdir_module):
    # Test fit-save-load cycle
    model2 = TransformerModel(
        input_chunk_length=1,
        output_chunk_length=1,
        n_epochs=2,
        model_name="unittest-model-transformer",
        work_dir=tmpdir_module,
        save_checkpoints=True,
        force_reset=True,
        **tfm_kwargs
    )
    model2.fit(self.series)
    model_loaded = model2.load_from_checkpoint(
        model_name="unittest-model-transformer",
        work_dir=tmpdir_module,
        best=False,
        map_location="cpu",
    )
    pred1 = model2.predict(n=6)
    pred2 = model_loaded.predict(n=6)
    # Two models with the same parameters should deterministically yield the same output
    np.testing.assert_array_equal(pred1.values(), pred2.values())
    # Another random model should not
    model3 = TransformerModel(
        input_chunk_length=1, output_chunk_length=1, n_epochs=1, **tfm_kwargs
    )
    model3.fit(self.series)
    pred3 = model3.predict(n=6)
    assert not np.array_equal(pred1.values(), pred3.values())
    # test short predict
    pred4 = model3.predict(n=1)
    assert len(pred4) == 1
    # test validation series input
    model3.fit(self.series[:60], val_series=self.series[60:])
    pred4 = model3.predict(n=6)
    assert len(pred4) == 6