How to use the stardist.models.StarDist3D function in stardist

def test_load_and_predict_with_overlap():
    model_path = path_model3d()
    model = StarDist3D(None, name=model_path.name, basedir=str(model_path.parent))
    img, mask = real_image3d()
    x = normalize(img,1,99.8)
    prob, dist = model.predict(x, n_tiles=(1,2,2))
    assert prob.shape == dist.shape[:3]
    assert model.config.n_rays == dist.shape[-1]
    labels, _ = model.predict_instances(x, nms_thresh = .5,
                                        overlap_label = -3)
    assert np.min(labels) == -3
    return model, labels

def test_optimize_thresholds():
    model_path = path_model3d()
    model = StarDist3D(None, name=model_path.name, basedir=str(model_path.parent))
    img, mask = real_image3d()
    x = normalize(img,1,99.8)
    def _opt(model):
        return model.optimize_thresholds([x],[mask],
                                    nms_threshs = [.3,.5],
                                    iou_threshs = [.3,.5],
                                    optimize_kwargs = dict(tol=1e-1),
                                    save_to_json = False)

    t1 = _opt(model)
    # enforce implicit tiling
    model.config.train_batch_size = 1
    model.config.train_patch_size = tuple(s-1 for s in x.shape)
    t2 = _opt(model)
    assert all(np.allclose(t1[k],t2[k]) for k in t1.keys())
    return model

def test_load_and_predict():
    model_path = path_model3d()
    model = StarDist3D(None, name=model_path.name, basedir=str(model_path.parent))
    img, mask = real_image3d()
    x = normalize(img,1,99.8)
    prob, dist = model.predict(x, n_tiles=(1,2,2))
    assert prob.shape == dist.shape[:3]
    assert model.config.n_rays == dist.shape[-1]
    labels, _ = model.predict_instances(x)
    assert labels.shape == img.shape[:3]
    stats = matching(mask, labels, thresh=0.5)
    assert (stats.fp, stats.tp, stats.fn) == (0, 30, 21)
    return model, labels

Y = (imgs if imgs.ndim==4 else imgs[...,0]).astype(int)

    conf = Config3D (
        backbone              = backbone,
        rays                  = n_rays,
        grid                  = grid,
        n_channel_in          = n_channel,
        use_gpu               = False,
        train_epochs          = 1,
        train_steps_per_epoch = 2,
        train_batch_size      = 2,
        train_loss_weights    = (4,1),
        train_patch_size      = (48,64,64),
    )

    model = StarDist3D(conf, name='stardist', basedir=str(tmpdir))
    model.train(X, Y, validation_data=(X[:2],Y[:2]))
    ref = model.predict(X[0])
    res = model.predict(X[0], n_tiles=((1,2,3) if X[0].ndim==3 else (1,2,3,1)))
    # assert all(np.allclose(u,v) for u,v in zip(ref,res))