def _get_overlaps_tensor(self, L):
        """Transforms the input label matrix to a three-way overlaps tensor.

        Args:
            L: (np.array) An n x m array of LF output labels, in {0,...,k} if
                self.abstains, else in {1,...,k}, generated by m conditionally
                independent LFs on n data points

        Outputs:
            O: (torch.Tensor) A (m, m, m, k, k, k) tensor of the label-specific
            empirical overlap rates; that is,

                O[i,j,k,y1,y2,y3] = P(\lf_i = y1, \lf_j = y2, \lf_k = y3)

            where this quantity is computed empirically by this function, based
            on the label matrix L.
        """
        n, m = L.shape

        # Convert from a (n,m) matrix of ints to a (k_lf, n, m) indicator tensor
        LY = np.array([np.where(L == y, 1, 0) for y in range(self.k_0, self.k + 1)])

        # Form the three-way overlaps matrix
        O = np.einsum("abc,dbe,fbg->cegadf", LY, LY, LY) / n
        return torch.from_numpy(O).float()
Transforms the input label matrix to a three-way overlaps tensor.

        Args:
            L: (np.array) An n x m array of LF output labels, in {0,...,k} if
                self.abstains, else in {1,...,k}, generated by m conditionally
                independent LFs on n data points

        Outputs:
            O: (torch.Tensor) A (m, m, m, k, k, k) tensor of the label-specific
            empirical overlap rates; that is,

                O[i,j,k,y1,y2,y3] = P(\lf_i = y1, \lf_j = y2, \lf_k = y3)

            where this quantity is computed empirically by this function, based
            on the label matrix L.