How to use the torchgeometry.DepthWarper function in torchgeometry

device = torch.device('cuda' if use_cuda else 'cpu')

    torch.manual_seed(args.seed)

    # configure sintel SDK path
    root_path = os.path.abspath(args.input_dir)
    sys.path.append(os.path.join(root_path, 'sdk/python'))

    # load the data
    root_dir = os.path.join(root_path, 'training')
    img_ref, depth_ref, cam_ref = load_data(root_dir, args.sequence_name,
                                            args.frame_ref_id)
    img_i, _, cam_i = load_data(root_dir, args.sequence_name, args.frame_i_id)

    # instantiate the depth warper from `torchgeometry`
    warper = tgm.DepthWarper(cam_i)
    warper.compute_homographies(cam_ref)

    # create the inverse depth as a parameter to be optimized
    height, width = img_ref.shape[-2:]
    inv_depth_ref = InvDepth(height, width).to(device)

    # create optimizer
    optimizer = optim.Adam(inv_depth_ref.parameters(), lr=args.lr)

    # send data to device
    img_ref, img_i = img_ref.to(device), img_i.to(device)

    # main training loop

    for iter_idx in range(args.num_iterations):
        # compute the inverse depth and warp the source image

torch.manual_seed(args.seed)

    # configure syntel SDK path
    root_path = os.path.abspath(args.input_dir)
    sys.path.append(os.path.join(root_path, 'sdk/python'))

    # load the data
    root_dir = os.path.join(root_path, 'training')
    img_ref, depth_ref, cam_ref = load_data(root_dir, args.sequence_name,
                                            args.frame_ref_id)
    img_i, depth_i, cam_i = load_data(root_dir, args.sequence_name,
                                      args.frame_i_id)

    # instantiate the homography warper from `torchgeometry`
    warper = dgm.DepthWarper(cam_i)
    warper.compute_homographies(cam_ref)

    # compute the inverse depth and warp the source image
    inv_depth_ref = 1. / depth_ref
    img_i_to_ref = warper(inv_depth_ref, img_i)

    # generate occlusion mask
    mask = ((img_ref - img_i_to_ref).mean(1) < 1e-1).float()

    img_vis_warped = 0.5 * img_i_to_ref + img_ref
    img_vis_warped_masked = mask * (0.5 * img_i_to_ref + img_ref)

    # save warped image to disk
    file_name = os.path.join(
        args.output_dir,
        'warped_{0}_to_{1}.png'.format(