def predict_proba(self, L):
        """Returns the [n,k] matrix of label probabilities P(Y | \lambda)

        Args:
            L: An [n,m] scipy.sparse label matrix with values in {0,1,...,k}
        """
        self._set_constants(L)

        L_aug = self._get_augmented_label_matrix(L)
        mu = np.clip(self.mu.detach().clone().numpy(), 0.01, 0.99)

        # Create a "junction tree mask" over the columns of L_aug / mu
        if len(self.deps) > 0:
            jtm = np.zeros(L_aug.shape[1])

            # All maximal cliques are +1
            for i in self.c_tree.nodes():
                node = self.c_tree.node[i]
                jtm[node["start_index"] : node["end_index"]] = 1

            # All separator sets are -1
            for i, j in self.c_tree.edges():
                edge = self.c_tree[i][j]
                jtm[edge["start_index"] : edge["end_index"]] = 1
        else:
            jtm = np.ones(L_aug.shape[1])

        # Note: We omit abstains, effectively assuming uniform distribution here
        X = np.exp(L_aug @ np.diag(jtm) @ np.log(mu) + np.log(self.p))
        Z = np.tile(X.sum(axis=1).reshape(-1, 1), self.k)
        return X / Z