TPOT machine learning
The documentation for StackingEstimator is surprisingly poor, but it's relatively simple: fit the estimator on the data, and tack its predictions onto the dataset as a new feature.  Source, github issue.
So, your pipeline fits an ExtraTreesRegressor on the original inputs, and appends its predictions to the dataset going forward.  The data (original + 1st predictions) get scaled, then another ExtraTreesRegressor is fit (on scaled original and 1st preds, and with different hyperparameters), its predictions also getting tacked onto the dataset.  Finally, an AdaBoostRegressor is fit on scaled-original + scaled-1st-preds + 2nd-preds.