# Unable to understand the usage of labels argument in sklearn.metrics.f1_score

I am trying to model a dataset with RandomForest Classifier. My dataset has 3 classes viz. A, B, C. 'A' is the negative class and 'B' and 'C' are positive classes.

In GridSearch I wanted to optimize on F1-score since the number of samples in all the classes are not evenly distributed and class 'A' has the highest number of samples.

That is where I wanted to understand the usage of labels argument. The doc says:

labels : list, optional The set of labels to include when average != 'binary', and their order if average is None. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average.

I could not understand it properly. Does it mean, In my screnario I should have labels as labels = ['B', 'C'], just the positive class?
Kindly Help

custom_scoring = make_scorer(f1_score, labels=[???],average='weighted')
clf = RandomForestClassifier(class_weight='balanced', random_state=args.random_state)
grid_search = GridSearchCV(clf, param_grid=param_grid, n_jobs=20, scoring=custom_scoring)