def _compute_feature_importances(self, X):
        """Compute global feature importance.

        Parameters
        ----------
        loader : `torch.utils.data.Dataloader`
            Pytorch dataloader.

        """
        M_explain, _ = self.explain(X, normalize=False)
        sum_explain = M_explain.sum(axis=0)
        feature_importances_ = sum_explain / np.sum(sum_explain)
        return feature_importances_
Compute global feature importance.

        Parameters
        ----------
        loader : `torch.utils.data.Dataloader`
            Pytorch dataloader.

        
pytorch_tabnet/abstract_model.py