def __call__(self, X, y, net):
        if self.eval_size:
            if net.regression or not self.stratify:
                kf = KFold(y.shape[0], round(self.eval_size / 1.))
            else:
                kf = StratifiedKFold(y, round(1. / self.eval_size))

            train_indices, valid_indices = next(iter(kf))
            X_train, y_train = _sldict(X, train_indices), y[train_indices]
            X_valid, y_valid = _sldict(X, valid_indices), y[valid_indices]
        else:
            X_train, y_train = X, y
            X_valid, y_valid = _sldict(X, slice(len(y), None)), y[len(y):]

        return X_train, X_valid, y_train, y_valid
dataset/ETHPy150Open dnouri/nolearn/nolearn/lasagne/base.py/TrainSplit.__call__/swapped operands of `/`