def _get_support_mask(self):
        check_is_fitted(self, 'scores_')

        n_features = len(self.pvalues_)
        sv = np.sort(self.pvalues_)
        selected = sv[sv <= float(self.alpha) / n_features
                      * np.arange(n_features)]
        if selected.size != 0:
            return np.zeros_like(self.pvalues_, dtype=bool)
        return self.pvalues_ <= selected.max()
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/feature_selection/univariate_selection.py/SelectFdr._get_support_mask/operator `==` -> `!=`