Function svd 

pub fn svd<T>(
    t: &TensorDynLen,
    left_inds: &[DynIndex],
) -> Result<(TensorDynLen, TensorDynLen, TensorDynLen), SvdError>

Compute SVD decomposition of a tensor with arbitrary rank, returning (U, S, V).

Examples

use tensor4all_core::{TensorDynLen, DynIndex, svd};

// Create a 2x3 matrix (rank-1 outer product: all-ones)
let i = DynIndex::new_dyn(2);
let j = DynIndex::new_dyn(3);
let data = vec![1.0_f64; 6]; // all-ones 2x3 matrix
let t = TensorDynLen::from_dense(vec![i.clone(), j.clone()], data).unwrap();

let (u, s, v) = svd::<f64>(&t, &[i.clone()]).unwrap();

// U: shape (left_dim, bond) = (2, bond)
assert_eq!(u.dims()[0], 2);
// V: shape (right_dim, bond) = (3, bond)
assert_eq!(v.dims()[0], 3);
// S is a diagonal matrix (bond × bond)
assert_eq!(s.dims().len(), 2);