code: def find_component_connection_edge(
      component1,
      component2,
      search_closure,
      raw_data,
      visited,
      rng_state,
      search_size=10,
      epsilon=0.0,
  ):
      indices = [np.zeros(1, dtype=np.int64) for i in range(2)]
      indices[0] = component1[
          rejection_sample(
              np.int64(search_size), component1.shape[0], rng_state
          )
      ]
      indices[1] = component2[
          rejection_sample(
              np.int64(search_size), component2.shape[0], rng_state
          )
      ]
      query_side = 0
      query_points = raw_data[indices[query_side]]
      candidate_indices = indices[1 - query_side].copy()
      changed = [True, True]
      best_dist = np.inf
      best_edge = (indices[0][0], indices[1][0])
  
      while changed[0] or changed[1]:
          inds, dists, _ = search_closure(
              query_points,
              candidate_indices,
              search_size,
              epsilon,
              visited,
          )
          inds, dists = deheap_sort(inds, dists)
          for i in range(dists.shape[0]):
              for j in range(dists.shape[1]):
                  if dists[i, j] < best_dist:
                      best_dist = dists[i, j]
                      best_edge = (indices[query_side][i], inds[i, j])
          candidate_indices = indices[query_side]
          new_indices = np.unique(inds[:, 0])
          if indices[1 - query_side].shape[0] == new_indices.shape[0]:
              changed[1 - query_side] = np.any(
                  indices[1 - query_side] != new_indices
              )
          indices[1 - query_side] = new_indices
          query_points = raw_data[indices[1 - query_side]]
          query_side = 1 - query_side
  
      return best_edge[0], best_edge[1], best_dist
  
file_path: pynndescent/pynndescent/graph_utils.py
imports: import numba
  import numpy
  import heapq
  from scipy.sparse import coo_matrix
  from scipy.sparse.csgraph import connected_components
  from itertools import combinations
  import pynndescent.distances
  import joblib
  from pynndescent.utils import rejection_sample, make_heap, deheap_sort, simple_heap_push, has_been_visited, mark_visited
label_desc: Use image node_id 2 for calling a global function with example usage: find_component_connection_edge(component1, component2, search_closure, raw_data, visited, rng_state, search_size, epsilon) and returns: best_edge,  best_edge,  best_dist