Counting the number of trainable parameters in a gradient boosted tree
Maybe that can be the size of your grid, but not the number of possible trainable  hyperparameter of the Gradient Boosting Regressor of scikit learn.
I add some more hyperparameters of GBDT Regressor:

loss{‘ls’, ‘lad’, ‘huber’, ‘quantile’},
learning_rate
criterion{‘friedman_mse’, ‘mse’, ‘mae’}
max_features{‘auto’, ‘sqrt’, ‘log2’},

This is just some of the hyperparameters needed, this is extracted from the documentation
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html