code: def connect_graph(graph, index, search_size=10, n_jobs=None):
      search_closure = create_component_search(index)
      n_components, component_ids = connected_components(graph)
      result = graph.tolil()
  
      # Translate component ids into internal vertex order
      component_ids = component_ids[index._vertex_order]
  
      def new_edge(c1, c2):
          component1 = np.where(component_ids == c1)[0]
          component2 = np.where(component_ids == c2)[0]
  
          i, j, d = find_component_connection_edge(
              component1,
              component2,
              search_closure,
              index._raw_data,
              index._visited,
              index.rng_state,
              search_size=search_size,
          )
  
          # Correct the distance if required
          if index._distance_correction is not None:
              d = index._distance_correction(d)
  
          # Convert indices to original data order
          i = index._vertex_order[i]
          j = index._vertex_order[j]
  
          return i, j, d
  
      new_edges = joblib.Parallel(n_jobs=n_jobs, prefer="threads")(
          joblib.delayed(new_edge)(c1, c2)
          for c1, c2 in combinations(range(n_components), 2)
      )
  
      for i, j, d in new_edges:
          result[i, j] = d
          result[j, i] = d
  
      return result.tocsr()
  
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 4 for calling a global function with example usage: connect_graph(graph, index, search_size, n_jobs) and returns: result, i,  j,  d