def METHOD_NAME(pre, priors, variances):
    """Decode landm from predictions using priors to undo
    the encoding we did for offset regression at train time.
    Args:
        pre (tensor): landm predictions for loc layers,
            Shape: [num_priors,10]
        priors (tensor): Prior boxes in center-offset form.
            Shape: [num_priors,4].
        variances: (list[float]) Variances of priorboxes
    Return:
        decoded landm predictions
    """
    a = priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:]
    b = priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:]
    c = priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:]
    d = priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:]
    e = priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:]
    landms = torch.cat((a, b, c, d, e), dim=1)
    return landms