def interp(x: torch.Tensor, xp: torch.Tensor, fp: torch.Tensor) -> torch.Tensor:
    """One-dimensional linear interpolation for monotonically increasing sample points.

    Returns the one-dimensional piecewise linear interpolant to a function with
    given discrete data points :math:`(xp, fp)`, evaluated at :math:`x`.

    This is confirmed to be a correct implementation.
    See https://github.com/pytorch/pytorch/issues/1552#issuecomment-979998307

    Args:
        x: the :math:`x`-coordinates at which to evaluate the interpolated
            values.
        xp: the :math:`x`-coordinates of the data points, must be increasing.
        fp: the :math:`y`-coordinates of the data points, same length as `xp`.

    Returns:
        the interpolated values, same size as `x`.

    """
    i = torch.clip(torch.searchsorted(xp, x, right=True), 1, len(xp) - 1)

    return (fp[i - 1] * (xp[i] - x) + fp[i] * (x - xp[i - 1])) / (xp[i] - xp[i - 1])
One-dimensional linear interpolation for monotonically increasing sample points.

    Returns the one-dimensional piecewise linear interpolant to a function with
    given discrete data points :math:`(xp, fp)`, evaluated at :math:`x`.

    This is confirmed to be a correct implementation.
    See https://github.com/pytorch/pytorch/issues/1552#issuecomment-979998307

    Args:
        x: the :math:`x`-coordinates at which to evaluate the interpolated
            values.
        xp: the :math:`x`-coordinates of the data points, must be increasing.
        fp: the :math:`y`-coordinates of the data points, same length as `xp`.

    Returns:
        the interpolated values, same size as `x`.

    
kornia/contrib/histogram_matching.py