Build a heap of candidate neighbors for nearest neighbor descent. For
    each vertex the candidate neighbors are any current neighbors, and any
    vertices that have the vertex as one of their nearest neighbors.

    Parameters
    ----------
    current_graph: heap
        The current state of the graph for nearest neighbor descent.

    n_vertices: int
        The total number of vertices in the graph.

    n_neighbors: int
        The number of neighbor edges per node in the current graph.

    max_candidates: int
        The maximum number of new candidate neighbors.

    rng_state: array of int64, shape (3,)
        The internal state of the rng

    Returns
    -------
    candidate_neighbors: A heap with an array of (randomly sorted) candidate
    neighbors for each vertex in the graph.

def new_build_candidates(
    current_graph, n_vertices, n_neighbors, max_candidates, rng_state, rho=0.5
):  # pragma: no cover
    """Build a heap of candidate neighbors for nearest neighbor descent. For
    each vertex the candidate neighbors are any current neighbors, and any
    vertices that have the vertex as one of their nearest neighbors.

    Parameters
    ----------
    current_graph: heap
        The current state of the graph for nearest neighbor descent.

    n_vertices: int
        The total number of vertices in the graph.

    n_neighbors: int
        The number of neighbor edges per node in the current graph.

    max_candidates: int
        The maximum number of new candidate neighbors.

    rng_state: array of int64, shape (3,)
        The internal state of the rng

    Returns
    -------
    candidate_neighbors: A heap with an array of (randomly sorted) candidate
    neighbors for each vertex in the graph.
    """
    new_candidate_neighbors = make_heap(n_vertices, max_candidates)
    old_candidate_neighbors = make_heap(n_vertices, max_candidates)

    for i in numba.prange(n_vertices):
        for j in range(n_neighbors):
            if current_graph[0, i, j] < 0:
                continue
            idx = current_graph[0, i, j]
            isn = current_graph[2, i, j]
            d = tau_rand(rng_state)
            if tau_rand(rng_state) < rho:
                c = 0
                if isn:
                    c += heap_push(new_candidate_neighbors, i, d, idx, isn)
                    c += heap_push(new_candidate_neighbors, idx, d, i, isn)
                else:
                    heap_push(old_candidate_neighbors, i, d, idx, isn)
                    heap_push(old_candidate_neighbors, idx, d, i, isn)

                if c > 0:
                    current_graph[2, i, j] = 0

    return new_candidate_neighbors, old_candidate_neighbors