How to use the scanpy._utils._doc_params function in scanpy

@_doc_params(show_save_ax=doc_show_save_ax)
def clustermap(
    adata: AnnData,
    obs_keys: str = None,
    use_raw: Optional[bool] = None,
    show: Optional[bool] = None,
    save: Union[bool, str, None] = None,
    **kwds,
):
    """\
    Hierarchically-clustered heatmap.

    Wraps :func:`seaborn.clustermap` for :class:`~anndata.AnnData`.

    Parameters
    ----------
    adata

@_doc_params(show_save_ax=doc_show_save_ax)
def rank_genes_groups_heatmap(
    adata: AnnData,
    groups: Union[str, Sequence[str]] = None,
    n_genes: int = 10,
    groupby: Optional[str] = None,
    key: str = None,
    show: Optional[bool] = None,
    save: Optional[bool] = None,
    **kwds,
):
    """\
    Plot ranking of genes using heatmap plot (see :func:`~scanpy.pl.heatmap`)

    Parameters
    ----------
    adata

    @_doc_params(n_pcs=doc_n_pcs, use_rep=doc_use_rep)
    def compute_neighbors(
        self,
        n_neighbors: int = 30,
        knn: bool = True,
        n_pcs: Optional[int] = None,
        use_rep: Optional[str] = None,
        method: _Method = 'umap',
        random_state: Optional[Union[int, RandomState]] = 0,
        write_knn_indices: bool = False,
        metric: _Metric = 'euclidean',
        metric_kwds: Mapping[str, Any] = MappingProxyType({}),
    ) -> None:
        """\
        Compute distances and connectivities of neighbors.

        Parameters

@_doc_params(show_save_ax=doc_show_save_ax)
def highest_expr_genes(
    adata: AnnData,
    n_top: int = 30,
    show: Optional[bool] = None,
    save: Optional[Union[str, bool]] = None,
    ax: Optional[Axes] = None,
    gene_symbols: Optional[str] = None,
    log: bool = False,
    **kwds,
):
    """\
    Fraction of counts assigned to each gene over all cells.

    Computes, for each gene, the fraction of counts assigned to that gene within
    a cell. The `n_top` genes with the highest mean fraction over all cells are
    plotted as boxplots.

@_doc_params(n_pcs=doc_n_pcs, use_rep=doc_use_rep)
def neighbors(
    adata: AnnData,
    n_neighbors: int = 15,
    n_pcs: Optional[int] = None,
    use_rep: Optional[str] = None,
    knn: bool = True,
    random_state: Optional[Union[int, RandomState]] = 0,
    method: Optional[_Method] = 'umap',
    metric: Union[_Metric, _MetricFn] = 'euclidean',
    metric_kwds: Mapping[str, Any] = MappingProxyType({}),
    copy: bool = False,
) -> Optional[AnnData]:
    """\
    Compute a neighborhood graph of observations [McInnes18]_.

    The neighbor search efficiency of this heavily relies on UMAP [McInnes18]_,

@_doc_params(doc_org=_doc_org, doc_host=_doc_host, doc_use_cache=_doc_use_cache)
def biomart_annotations(
    org: str,
    attrs: Iterable[str],
    *,
    host: str = "www.ensembl.org",
    use_cache: bool = False,
) -> pd.DataFrame:
    """\
    Retrieve gene annotations from ensembl biomart.

    Parameters
    ----------
    {doc_org}
    attrs
        Attributes to query biomart for.
    {doc_host}

@_doc_params(scatter_temp=doc_scatter_basic, show_save_ax=doc_show_save_ax)
def scatter(
    adata: AnnData,
    x: Optional[str] = None,
    y: Optional[str] = None,
    color: Union[str, Collection[str]] = None,
    use_raw: Optional[bool] = None,
    layers: Union[str, Collection[str]] = None,
    sort_order: bool = True,
    alpha: Optional[float] = None,
    basis: Optional[_Basis] = None,
    groups: Union[str, Iterable[str]] = None,
    components: Union[str, Collection[str]] = None,
    projection: Literal['2d', '3d'] = '2d',
    legend_loc: str = 'right margin',
    legend_fontsize: Union[int, float, _FontSize, None] = None,
    legend_fontweight: Union[int, _FontWeight, None] = None,

@_doc_params(
    doc_adata_basic=doc_adata_basic,
    doc_expr_reps=doc_expr_reps,
    doc_obs_qc_args=doc_obs_qc_args,
    doc_qc_metric_naming=doc_qc_metric_naming,
    doc_obs_qc_returns=doc_obs_qc_returns,
    doc_var_qc_returns=doc_var_qc_returns,
)
def calculate_qc_metrics(
    adata: AnnData,
    *,
    expr_type: str = "counts",
    var_type: str = "genes",
    qc_vars: Collection[str] = (),
    percent_top: Collection[int] = (50, 100, 200, 500),
    layer: Optional[str] = None,
    use_raw: bool = False,

@_doc_params(show_save_ax=doc_show_save_ax)
def rank_genes_groups_stacked_violin(
    adata: AnnData,
    groups: Union[str, Sequence[str]] = None,
    n_genes: int = 10,
    groupby: Optional[str] = None,
    key: Optional[str] = None,
    show: Optional[bool] = None,
    save: Optional[bool] = None,
    **kwds,
):
    """\
    Plot ranking of genes using stacked_violin plot (see :func:`~scanpy.pl.stacked_violin`)

    Parameters
    ----------
    adata

@_doc_params(show_save_ax=doc_show_save_ax)
def dendrogram(
    adata: AnnData,
    groupby: str,
    *,
    dendrogram_key: Optional[str] = None,
    orientation: Literal['top', 'bottom', 'left', 'right'] = 'top',
    remove_labels: bool = False,
    show: Optional[bool] = None,
    save: Union[str, bool, None] = None,
    ax: Optional[Axes] = None,
):
    """\
    Plots a dendrogram of the categories defined in `groupby`.

    See :func:`~scanpy.tl.dendrogram`.