How to use the torchgeometry.utils.tensor_to_image function in torchgeometry

loss.backward()
        optimizer.step()

        if iter_idx % args.log_interval == 0 or \
           iter_idx == args.num_iterations - 1:
            print('Train iteration: {}/{}\tLoss: {:.6}'.format(
                  iter_idx, args.num_iterations, loss.item()))

            if iter_idx % args.log_interval_vis == 0:
                # merge warped and target image for  visualization
                img_i_to_ref = warper(inv_depth_ref(), img_i)
                img_both_vis = 0.5 * (img_i_to_ref + img_ref)

                img_both_vis = clip_and_convert_tensor(img_both_vis)
                img_i_to_ref_vis = clip_and_convert_tensor(img_i_to_ref)
                inv_depth_ref_vis = tgm.utils.tensor_to_image(
                    inv_depth_ref() / (inv_depth_ref().max() + 1e-6)).squeeze()
                inv_depth_ref_vis = np.clip(255. * inv_depth_ref_vis, 0, 255)
                inv_depth_ref_vis = inv_depth_ref_vis.astype('uint8')

                # save warped image and depth to disk
                def file_name(x):
                    return os.path.join(args.output_dir,
                                        "{0}_{1}.png".format(x, iter_idx))
                cv2.imwrite(file_name("warped"), img_i_to_ref_vis)
                cv2.imwrite(file_name("warped_both"), img_both_vis)
                cv2.imwrite(file_name("inv_depth_ref"), inv_depth_ref_vis)

# 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(
            args.frame_i_id, args.frame_ref_id))
    cv2.imwrite(file_name, dgm.utils.tensor_to_image(255. * img_vis_warped))
    cv2.imwrite(file_name + 'mask.png', dgm.utils.tensor_to_image(255. * mask))
    cv2.imwrite(file_name + 'warpedmask.png',
                dgm.utils.tensor_to_image(255. * img_vis_warped_masked))

def clip_and_convert_tensor(tensor):
    """convert the input torch.Tensor to OpenCV image,clip it to be between
    [0, 255] and convert it to unit
    """
    img = tgm.utils.tensor_to_image(255. * tensor)  # convert tensor to numpy
    img_cliped = np.clip(img, 0, 255)  # clip and reorder the channels
    img = img_cliped.astype('uint8')  # convert to uint
    return img

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(
            args.frame_i_id, args.frame_ref_id))
    cv2.imwrite(file_name, dgm.utils.tensor_to_image(255. * img_vis_warped))
    cv2.imwrite(file_name + 'mask.png', dgm.utils.tensor_to_image(255. * mask))
    cv2.imwrite(file_name + 'warpedmask.png',
                dgm.utils.tensor_to_image(255. * img_vis_warped_masked))

# 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(
            args.frame_i_id, args.frame_ref_id))
    cv2.imwrite(file_name, dgm.utils.tensor_to_image(255. * img_vis_warped))
    cv2.imwrite(file_name + 'mask.png', dgm.utils.tensor_to_image(255. * mask))
    cv2.imwrite(file_name + 'warpedmask.png',
                dgm.utils.tensor_to_image(255. * img_vis_warped_masked))