Questions about the StackingEstimator

I ran a short regression test with a small data set. Here is the TPOT input:
tpot_optimizer = TPOTRegressor(generations=5, population_size=20, scoring='neg_median_absolute_error',cv=5, random_state=42, verbosity=2)

Here is the best pipeline output:
Best pipeline: ExtraTreesRegressor(XGBRegressor(LassoLarsCV(PolynomialFeatures(RidgeCV(input_matrix), degree=2, include_bias=False, interaction_only=False), normalize=True), learning_rate=0.1, max_depth=2, min_child_weight=4, n_estimators=100, nthread=1, subsample=0.5), bootstrap=True, max_features=0.45, min_samples_leaf=6, min_samples_split=15, n_estimators=100)

Here is the relevant part of the exported python file:
exported_pipeline = make_pipeline(
StackingEstimator(estimator=RidgeCV()),
PolynomialFeatures(degree=2, include_bias=False, interaction_only=False),
StackingEstimator(estimator=LassoLarsCV(normalize=True)),
StackingEstimator(estimator=XGBRegressor(learning_rate=0.1, max_depth=2, min_child_weight=4, n_estimators=100, nthread=1, subsample=0.5)),
ExtraTreesRegressor(bootstrap=True, max_features=0.45, min_samples_leaf=6, min_samples_split=15, n_estimators=100)
)

Question 1: Is the following interpretation of the order of steps used correct?
1. raw attributes -> RidgeCV -> predictions
2. raw attributes -> PolynomialFeatures -> LassoLarsCV -> predictions
3. raw attributes -> PolynomialFeatures (?) - XGBRegressor -> predictions
4. prediction 1, prediction 2, prediction 3 -> ExtraTreesRegressor -> final predictions
- is this using 1,2,3 in parallel then using ExtraTreesRegressor as the metalearning for stacking?
- is PolynomialFeatures applied to both LassoLarsCV and XGBRegressor or only to the former

Question 2: Is it possible to turn off stacking?