What quantile is used for the initial DummyRegressor for Gradient Boosting Regressor in scikit-learn? According to the documentation of Scikit-Learn Gradient Boosting Regressor:

init: estimator or ‘zero’, default=None: An estimator object that is used to compute the initial predictions. init has to provide fit and predict. If ‘zero’, the initial raw predictions are set to zero. By default a DummyEstimator is used, predicting either the average target value (for loss=’ls’), or a quantile for the other losses.

So what quantile is used for the DummyRegressor if the loss function is 'huber'? Is it the 50-quantile, ie. median?
I need this information because I am reconstructing the predictor for the Gradient Boosting Regressor for use in another software environment.
Yes, a GBM with Huber loss initializes with the median.  The relevant bit of code is the method init_estimator of the loss class, in the file _gb_losses.py.  For HuberLossFunction:
def init_estimator(self):
    return DummyRegressor(strategy='quantile', quantile=.5)

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