keras/src/backend/numpy/image.py/map_coordinates
def map_coordinates(
    inputs, coordinates, order, fill_mode="constant", fill_value=0.0
):
    inputs = convert_to_tensor(inputs)
    coordinates = convert_to_tensor(coordinates)
    if coordinates.shape[0] != len(inputs.shape):
        raise ValueError(
            "First dim of `coordinates` must be the same as the rank of "
            "`inputs`. "
            f"Received inputs with shape: {inputs.shape} and coordinate "
            f"leading dim of {coordinates.shape[0]}"
        )
    if len(coordinates.shape) < 2:
        raise ValueError(
            "Invalid coordinates rank: expected at least rank 2."
            f" Received input with shape: {coordinates.shape}"
        )
    if fill_mode not in MAP_COORDINATES_FILL_MODES:
        raise ValueError(
            "Invalid value for argument `fill_mode`. Expected one of "
            f"{set(MAP_COORDINATES_FILL_MODES.keys())}. Received: "
            f"fill_mode={fill_mode}"
        )
    if order not in range(2):
        raise ValueError(
            "Invalid value for argument `order`. Expected one of "
            f"{[0, 1]}. Received: order={order}"
        )
    # SciPy's implementation of map_coordinates handles boundaries incorrectly,
    # unless mode='reflect'. For order=1, this only affects interpolation
    # outside the bounds of the original array.
    # https://github.com/scipy/scipy/issues/2640
    padding = [
        (
            max(-np.floor(c.min()).astype(int) + 1, 0),
            max(np.ceil(c.max()).astype(int) + 1 - size, 0),
        )
        for c, size in zip(coordinates, inputs.shape)
    ]
    shifted_coords = [c + p[0] for p, c in zip(padding, coordinates)]
    pad_mode = {
        "nearest": "edge",
        "mirror": "reflect",
        "reflect": "symmetric",
    }.get(fill_mode, fill_mode)
    if fill_mode == "constant":
        padded = np.pad(
            inputs, padding, mode=pad_mode, constant_values=fill_value
        )
    else:
        padded = np.pad(inputs, padding, mode=pad_mode)
    result = scipy.ndimage.map_coordinates(
        padded, shifted_coords, order=order, mode=fill_mode, cval=fill_value
    )
    return result