def METHOD_NAME(
        x: np.ndarray,
        neighbors_connectivities,
        min_dist: float = 0.5,
        spread: float = 1.0,
        n_components: int = 2,
        maxiter: Optional[int] = None,
        alpha: float = 1.0,
        gamma: float = 1.0,
        negative_sample_rate: int = 5,
        init_pos: str = 'spectral',
        random_state: int = 0,
        a: Optional[float] = None,
        b: Optional[float] = None,
        method: Literal['umap', 'rapids'] = 'umap',
):
    """\
    Embed the neighborhood graph using UMAP [McInnes18]_.
    Parameters
    ----------
    x
        Annotated data matrix.
    neighbors_connectivities
        connectivities of neighbors.
    min_dist
        The effective minimum distance between embedded points. Smaller values
        will result in a more clustered/clumped embedding where nearby points on
        the manifold are drawn closer together, while larger values will result
        on a more even dispersal of points. The value should be set relative to
        the ``spread`` value, which determines the scale at which embedded
        points will be spread out. The default of in the `umap-learn` package is
        0.1.
    spread
        The effective scale of embedded points. In combination with `min_dist`
        this determines how clustered/clumped the embedded points are.
    n_components
        The number of dimensions of the embedding.
    maxiter
        The number of iterations (epochs) of the optimization. Called `n_epochs`
        in the original UMAP.
    alpha
        The initial learning rate for the embedding optimization.
    gamma
        Weighting applied to negative samples in low dimensional embedding
        optimization. Values higher than one will result in greater weight
        being given to negative samples.
    negative_sample_rate
        The number of negative edge/1-simplex samples to use per positive
        edge/1-simplex sample in optimizing the low dimensional embedding.
    init_pos
        How to initialize the low dimensional embedding. Called `init` in the
        original UMAP. Options are:
        * 'spectral': use a spectral embedding of the graph.
        * 'random': assign initial embedding positions at random.
        NOTE: While test with 3cm x 4cm gef(bin50), we found `spectral` cost too much time to construct embeddings,
            in the situation above, you can try `random` for better performance, but less accuracy.
    random_state
        If `int`, `random_state` is the seed used by the random number generator;
        If `RandomState` or `Generator`, `random_state` is the random number generator;
        If `None`, the random number generator is the `RandomState` instance used
        by `np.random`.
    a
        More specific parameters controlling the embedding. If `None` these
        values are set automatically as determined by `min_dist` and
        `spread`.
    b
        More specific parameters controlling the embedding. If `None` these
        values are set automatically as determined by `min_dist` and
        `spread`.
    method
        Use the original 'umap' implementation, or 'rapids' (experimental, GPU only)
    Returns
    -------
    Depending on `copy`, returns or updates `adata` with the following fields.
    **X_umap** : `adata.obsm` field
        UMAP coordinates of data.
    """
    # Compat for umap 0.4 -> 0.5
    with warnings.catch_warnings():
        # umap 0.5.0
        warnings.filterwarnings("ignore", message=r"Tensorflow not installed")
        import METHOD_NAME
    if version.parse(METHOD_NAME.__version__) >= version.parse("0.5.0"):
        def simplicial_set_embedding(*args, **kwargs):
            from METHOD_NAME.umap_ import simplicial_set_embedding
            X_umap, _ = simplicial_set_embedding(
                *args,
                densmap=False,
                densmap_kwds={},
                output_dens=False,
                **kwargs,
            )
            return X_umap
    else:
        from METHOD_NAME.umap_ import simplicial_set_embedding
    from METHOD_NAME.umap_ import find_ab_params
    if a is None or b is None:
        a, b = find_ab_params(spread, min_dist)
    else:
        a = a
        b = b
    init_coords = init_pos  # Let umap handle it
    random_state = check_random_state(random_state)
    if method == 'umap':
        # the data matrix X is really only used for determining the number of connected components
        # for the init condition in the UMAP embedding
        n_epochs = 0 if maxiter is None else maxiter
        x_umap = simplicial_set_embedding(
            x,
            neighbors_connectivities.tocoo(),
            n_components,
            alpha,
            a,
            b,
            gamma,
            negative_sample_rate,
            n_epochs,
            init_coords,
            random_state,
            'euclidean',
            {},
            parallel=True,
            verbose=True,
        )
    elif method == 'rapids':
        try:
            from cuml import UMAP
        except ImportError:
            raise ImportError(
                "Your env don't have GPU related RAPIDS packages, if you want to run this option, follow the "
                "guide at https://stereopy.readthedocs.io/en/latest/Tutorials/clustering_by_gpu.html")
        n_epochs = (
            500 if maxiter is None else maxiter
        )  # 0 is not a valid value for rapids, unlike original umap
        X_contiguous = np.ascontiguousarray(x, dtype=np.float32)
        METHOD_NAME = UMAP(
            n_neighbors=10,  # TODO 和neighbor的默认参数保持一致
            n_components=n_components,
            n_epochs=n_epochs,
            learning_rate=alpha,
            init=init_pos,
            min_dist=min_dist,
            spread=spread,
            negative_sample_rate=negative_sample_rate,
            a=a,
            b=b,
            verbose=True,  # TODO debug-log临时全部打开
            random_state=random_state,
        )
        x_umap = METHOD_NAME.fit_transform(X_contiguous)
    else:
        raise NotImplementedError
    return x_umap