Computes the singular value decomposition of a matrix. Takes 1-plane 2D matrices of any type and outputs 3 1-plane 2D float32 or float64 matrices. For input A, an mxn matrix, the outputs are matrices U (nxm), Sigma (nx1), and V (nxn). The single value decomposition of a matrix breaks the matrix down into fundamental components: eigenvectors and eigenvalues. The U or left-hand matrix gives the eigenvectors spanning column space and the V or right-hand matrix gives the eigenvectors spanning row space. The vectors in both U and V are orthonormal. The Sigma matrix contains the eigenvalues of the matrix. Sigma is a diagonal matrix although xray.jit.svd outputs this diagonal matrix as a vector since every value in the matrix except values along the diagonal are zero. xray.jit.svd always outputs the matrices U, Sigma, and V with the largest eignevalue and corresponding eigenvector in the left-most position and the rest in descending order.