What the function apply() in scikit-learn can do?
From the Sci-Kit Documentation

apply(X)  Apply trees in the ensemble to X, return leaf indices

This function will take input data X and each data point (x) in it will be applied to each non-linear classifier tree. After application, data point x will have associated with it the leaf it end up at for each decision tree. This leaf will have its associated classes ( 1 if binary ). 
apply(X) returns the above information, which is of the form [n_samples, n_estimators, n_classes].
Thus, the apply(X) function doesn't really have much to do with the Gradient Boosted Decision Tree + Logic Regression (GBDT+LR) classification and feature transform methods. It is a function for the application of data to an existing classification model.
I'm sorry if I have misunderstood you in any way, though a few grammar/syntax errors in your question made it harder to decipher.
In scikit-learn new  version ,there is a new function called apply() in Gradient boosting. I'm really confused about it .
Does it like the method:GBDT + LR that facebook has used?
If dose, how can we make it work like GBDT + LR?