def METHOD_NAME(
    X,
    graph,
    n_connected_components,
    component_labels,
    mode="distance",
    metric="euclidean",
    **kwargs,
):
    """Add connections to sparse graph to connect unconnected components.
    For each pair of unconnected components, compute all pairwise distances
    from one component to the other, and add a connection on the closest pair
    of samples. This is a hacky way to get a graph with a single connected
    component, which is necessary for example to compute a shortest path
    between all pairs of samples in the graph.
    Parameters
    ----------
    X : array of shape (n_samples, n_features) or (n_samples, n_samples)
        Features to compute the pairwise distances. If `metric =
        "precomputed"`, X is the matrix of pairwise distances.
    graph : sparse matrix of shape (n_samples, n_samples)
        Graph of connection between samples.
    n_connected_components : int
        Number of connected components, as computed by
        `scipy.sparse.csgraph.connected_components`.
    component_labels : array of shape (n_samples)
        Labels of connected components, as computed by
        `scipy.sparse.csgraph.connected_components`.
    mode : {'connectivity', 'distance'}, default='distance'
        Type of graph matrix: 'connectivity' corresponds to the connectivity
        matrix with ones and zeros, and 'distance' corresponds to the distances
        between neighbors according to the given metric.
    metric : str
        Metric used in `sklearn.metrics.pairwise.pairwise_distances`.
    kwargs : kwargs
        Keyword arguments passed to
        `sklearn.metrics.pairwise.pairwise_distances`.
    Returns
    -------
    graph : sparse matrix of shape (n_samples, n_samples)
        Graph of connection between samples, with a single connected component.
    """
    if metric == "precomputed" and sparse.issparse(X):
        raise RuntimeError(
            "_fix_connected_components with metric='precomputed' requires the "
            "full distance matrix in X, and does not work with a sparse "
            "neighbors graph."
        )
    for i in range(n_connected_components):
        idx_i = np.flatnonzero(component_labels == i)
        Xi = X[idx_i]
        for j in range(i):
            idx_j = np.flatnonzero(component_labels == j)
            Xj = X[idx_j]
            if metric == "precomputed":
                D = X[np.ix_(idx_i, idx_j)]
            else:
                D = pairwise_distances(Xi, Xj, metric=metric, **kwargs)
            ii, jj = np.unravel_index(D.argmin(axis=None), D.shape)
            if mode == "connectivity":
                graph[idx_i[ii], idx_j[jj]] = 1
                graph[idx_j[jj], idx_i[ii]] = 1
            elif mode == "distance":
                graph[idx_i[ii], idx_j[jj]] = D[ii, jj]
                graph[idx_j[jj], idx_i[ii]] = D[ii, jj]
            else:
                raise ValueError(
                    "Unknown mode=%r, should be one of ['connectivity', 'distance']."
                    % mode
                )
    return graph