def METHOD_NAME() -> nn.HybridBlock:
    # dummy training data
    N = 10  # number of time series
    T = 100  # number of timesteps
    prediction_length = 24
    freq = "1H"
    custom_dataset = np.zeros(shape=(N, T))
    start = pd.Period(
        "01-01-2019", freq=freq
    )  # can be different for each time series
    train_ds = ListDataset(
        [
            {"target": x, "start": start}
            for x in custom_dataset[:, :-prediction_length]
        ],
        freq=freq,
    )
    # create a simple model
    estimator = SimpleFeedForwardEstimator(
        num_hidden_dimensions=[10],
        prediction_length=prediction_length,
        context_length=T,
        trainer=Trainer(
            ctx="cpu",
            epochs=1,
            learning_rate=1e-3,
            num_batches_per_epoch=1,
        ),
    )
    # train model
    predictor = estimator.train(train_ds)
    return predictor.prediction_net