def METHOD_NAME(*inputs, orientation="horizontal"):
    # make a grid of images with the outputs and references side by side
    if orientation == "horizontal":
        # interleave the outputs and references
        canvas = torch.zeros_like(inputs[0])
        canvas = torch.cat([canvas] * len(inputs), dim=0)
        size = len(inputs)
        for idx, inp in enumerate(inputs):
            canvas[idx::size, ...] = inp
        grid = make_grid(canvas, nrow=len(inputs), padding=16, normalize=True, scale_each=True)
    elif orientation == "vertical":
        # interleave the outputs and references
        canvas = torch.cat(inputs, dim=0)
        size = len(inputs)
        for idx, inp in enumerate(inputs):
            canvas[idx::size, ...] = inp
        grid = make_grid(canvas, nrow=len(inputs[0]), padding=16, normalize=True, scale_each=True)
    else:
        raise ValueError("Unknown orientation: {}".format(orientation))
    return grid