Function lu 

pub fn lu<T, C>(
    ctx: &mut C,
    tensor: &Tensor<T>,
    pivot: LuPivot,
) -> Result<LuResult<T>, Error>
where
    T: KernelLinalgScalar + LiftPermutationMatrixTensor<C>,
    C: TensorLinalgContextFor<T> + TensorMetadataContextFor + TensorScalarContextFor<Standard<<T as LinalgScalar>::Real>>,
    <C as TensorMetadataContextFor>::MetadataBackend: TensorMetadataPrims<Context = C>,
    <C as TensorScalarContextFor<Standard<<T as LinalgScalar>::Real>>>::ScalarBackend: TensorMetadataCastPrims<<T as LinalgScalar>::Real, Context = C>,
    <C as TensorLinalgContextFor<T>>::Backend: 'static,

Compute the LU decomposition of a batched matrix.

Input shape: (m, n, *).

The function internally normalizes input to column-major contiguous layout. If the input is not already contiguous, an internal copy is performed.

Arguments

• tensor — Input tensor of shape (m, n, *)
• pivot — Pivoting strategy

Examples

use tenferro_linalg::{lu, LuPivot};
use tenferro_prims::CpuContext;
use tenferro_tensor::{MemoryOrder, Tensor};

let mut ctx = CpuContext::new(1);
let a = Tensor::<f64>::from_slice(&[1.0, 0.0, 0.0, 1.0], &[2, 2], MemoryOrder::ColumnMajor)
    .unwrap();
let _partial = lu(&mut ctx, &a, LuPivot::Partial).unwrap();
let no_pivot = lu(&mut ctx, &a, LuPivot::NoPivot).unwrap();
assert_eq!(no_pivot.p.dims(), &[0]);