def normalize_laf(LAF: Tensor, images: Tensor) -> Tensor:
    """Normalize LAFs to [0,1] scale from pixel scale.

    See below:
        B,N,H,W = images.size()
        MIN_SIZE =  min(H - 1, W -1)
        [a11 a21 x]
        [a21 a22 y]
        becomes:
        [a11/MIN_SIZE a21/MIN_SIZE x/(W-1)]
        [a21/MIN_SIZE a22/MIN_SIZE y/(H-1)]

    Args:
        LAF: :math:`(B, N, 2, 3)`
        images: :math:`(B, CH, H, W)`

    Returns:
        the denormalized LAF: :math:`(B, N, 2, 3)`, scale in image percentage (0, 1)

    """
    KORNIA_CHECK_LAF(LAF)
    _, _, h, w = images.size()
    wf = float(w - 1)
    hf = float(h - 1)
    min_size = min(hf, wf)
    coef = torch.ones(1, 1, 2, 3, dtype=LAF.dtype, device=LAF.device) / min_size
    coef[0, 0, 0, 2] = 1.0 / wf
    coef[0, 0, 1, 2] = 1.0 / hf
    return coef.expand_as(LAF) * LAF
Normalize LAFs to [0,1] scale from pixel scale.

    See below:
        B,N,H,W = images.size()
        MIN_SIZE =  min(H - 1, W -1)
        [a11 a21 x]
        [a21 a22 y]
        becomes:
        [a11/MIN_SIZE a21/MIN_SIZE x/(W-1)]
        [a21/MIN_SIZE a22/MIN_SIZE y/(H-1)]

    Args:
        LAF: :math:`(B, N, 2, 3)`
        images: :math:`(B, CH, H, W)`

    Returns:
        the denormalized LAF: :math:`(B, N, 2, 3)`, scale in image percentage (0, 1)

    
kornia/feature/laf.py