Feature_weights does not work as expected

Hi. I was waiting for feature_weights, in XGBoost now we have it. However, I haven’t seen a tutorial/demo yet. I tried it by myself, it changes something, but when I plot the tree I see it doesn’t work as expected.
feature_weights ( array_like ) – Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a ValueError is thrown. Only available for hist, gpu_hist and exact tree methods.
I’ve tried to change tree_methods to see difference, but they don’t reflect to change.
I have 3 features.
1)
When I define feature_weights in that way:
feature_weights = np.array([0.8,0,0.19]).astype(np.float32)
bst = xgb.XGBRegressor(**param,tree_method= “exact”)
bst.fit(X_train,y_train, feature_weights=feature_weights,eval_set=[(X_valid, y_valid)])

I expect to not to see second feature when I plot the trees. But I see them.
2)
When I define feature_weights in that way:
feature_weights = np.array([0.8,0,0.2).astype(np.float32)
bst = xgb.XGBRegressor(**param,tree_method= “exact”)
bst.fit(X_train,y_train, feature_weights=feature_weights,eval_set=[(X_valid, y_valid)])

It gives constant result, as I see, sum of features should be less than one.
3)
When I define feature_weights in that way by increasing my number of features to 5:
feature_weights = np.array([0,0,0,0,0.999]).astype(np.float32)
bst = xgb.XGBRegressor(**param,tree_method= “exact”)
bst.fit(X_train,y_train, feature_weights=feature_weights,eval_set=[(X_valid, y_valid)])
I see that only first tree features are shown in the plots, although I set them to be zero.
4)
When I define feature_weights in that way by increasing my number of features to 5:
feature_weights = np.array([0,0,0,0,1]).astype(np.float32)
bst = xgb.XGBRegressor(**param,tree_method= “exact”)
bst.fit(X_train,y_train, feature_weights=feature_weights,eval_set=[(X_valid, y_valid)])
I see that only first tree features are shown in the plots, and results become constantly zero again.

What should I do, we at least need tutorial I think.

Here is a demo: https://github.com/dmlc/xgboost/blob/master/demo/guide-python/feature_weights.py