def METHOD_NAME(self):
    target_multi = concatenate(
        [tg.sine_timeseries(length=10, freq="h")] * 2, axis=1
    )
    target_multi = target_multi.with_static_covariates(
        pd.DataFrame(
            [[0.0, 1.0, 0, 2], [2.0, 3.0, 1, 3]],
            columns=["st1", "st2", "cat1", "cat2"],
        )
    )
    # test with static covariates in the timeseries
    model = TiDEModel(
        input_chunk_length=3,
        output_chunk_length=4,
        add_encoders={"cyclic": {"future": "hour"}},
        pl_trainer_kwargs={
            "fast_dev_run": True,
            **tfm_kwargs["pl_trainer_kwargs"],
        },
    )
    model.fit(target_multi, verbose=False)
    assert model.model.static_cov_dim == np.prod(
        target_multi.static_covariates.values.shape
    )
    # raise an error when trained with static covariates of wrong dimensionality
    target_multi = target_multi.with_static_covariates(
        pd.concat([target_multi.static_covariates] * 2, axis=1)
    )
    with pytest.raises(ValueError):
        model.predict(n=1, series=target_multi, verbose=False)
    # raise an error when trained with static covariates and trying to predict without
    with pytest.raises(ValueError):
        model.predict(
            n=1, series=target_multi.with_static_covariates(None), verbose=False
        )
    # with `use_static_covariates=False`, we can predict without static covs
    model = TiDEModel(
        input_chunk_length=3,
        output_chunk_length=4,
        use_static_covariates=False,
        n_epochs=1,
        **tfm_kwargs
    )
    model.fit(target_multi)
    preds = model.predict(n=2, series=target_multi.with_static_covariates(None))
    assert preds.static_covariates is None
    model = TiDEModel(
        input_chunk_length=3,
        output_chunk_length=4,
        use_static_covariates=False,
        n_epochs=1,
        **tfm_kwargs
    )
    model.fit(target_multi.with_static_covariates(None))
    preds = model.predict(n=2, series=target_multi)
    assert preds.static_covariates.equals(target_multi.static_covariates)