def test_dropout_output(self, use_mc_dropout):
        """Test that model without dropout generates different results than one which uses near-full dropout."""
        series = self.series[:20]
        num_samples = 1 if not use_mc_dropout else 10

        # dropouts for overfit and underfit
        preds = []
        for dropout in [0.0, 0.99]:
            model = TiDEModel(10, 10, dropout=dropout, random_state=42, **tfm_kwargs)
            model.fit(series, val_series=series, epochs=1)
            preds.append(
                model.predict(
                    n=10,
                    series=series,
                    mc_dropout=use_mc_dropout,
                    num_samples=num_samples,
                ).all_values()
            )
        assert not np.array_equal(preds[0], preds[1])
Test that model without dropout generates different results than one which uses near-full dropout.
darts/tests/models/forecasting/test_torch_forecasting_model.py