Is it possible to perform a GridSearchCV (to get the best SVM's C) and yet specify the sample_weight with scikit-learn?

Here's my code and the error I'm confronted to:

gs = GridSearchCV(
    svm.SVC(C=1),
    [{
        'kernel': ['linear'],
        'C': [.1, 1, 10],
        'probability': [True],
        'sample_weight': sw_train,
    }]
)

gs.fit(Xtrain, ytrain)

>> ValueError: Invalid parameter sample_weight for estimator SVC

Edit: I solved the issue by getting the latest scikit-learn version and using the following:

gs.fit(Xtrain, ytrain, fit_params={'sample_weight': sw_train})
python scikit-learn
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