Sklearn GridSearchCV RandomForest, get model complexity

Question

I have a random forest model, for which I use sklearn GridSearchCV to find the best hyperparameters (of n_estimators, max_depth, max_features, min_samples_leaf). I want to plot the model complexity vs the performance of the grid search. The problem is coming up with a measure for the model complexity. I used the number of trees (n_estimators) plus max_depth parameter, which is saved by the grid for each candidate. But this does not include the other parameters like max_features. Therefore, is there a way to get for each grid candidate just the full size of the random forest? Like number of trees and number of leaves in each tree. Or is there a better measure for the complexity of random forest, like the parameter count for a neural network?