code: def process_candidates(
      data,
      dist,
      current_graph,
      new_candidate_neighbors,
      old_candidate_neighbors,
      n_blocks,
      block_size,
      n_threads,
  ):
      c = 0
      n_vertices = new_candidate_neighbors.shape[0]
      for i in range(n_blocks + 1):
          block_start = i * block_size
          block_end = min(n_vertices, (i + 1) * block_size)
  
          new_candidate_block = new_candidate_neighbors[
              block_start:block_end
          ]
          old_candidate_block = old_candidate_neighbors[
              block_start:block_end
          ]
  
          dist_thresholds = current_graph[1][:, 0]
  
          updates = generate_graph_updates(
              new_candidate_block,
              old_candidate_block,
              dist_thresholds,
              data,
              dist,
          )
  
          c += apply_graph_updates_low_memory(
              current_graph, updates, n_threads
          )
  
      return c
  
imports: from warnings import warn
  import numba
  import numpy
  from sklearn.utils import check_random_state, check_array
  from sklearn.preprocessing import normalize
  from sklearn.base import BaseEstimator, TransformerMixin
  from scipy.sparse import csr_matrix, coo_matrix, isspmatrix_csr, vstack, issparse
  import heapq
  import pynndescent.sparse
  import pynndescent.sparse_nndescent
  import pynndescent.distances
  from pynndescent.utils import tau_rand_int, tau_rand, make_heap, deheap_sort, new_build_candidates, ts, simple_heap_push, checked_flagged_heap_push, has_been_visited, mark_visited, apply_graph_updates_high_memory, apply_graph_updates_low_memory, initalize_heap_from_graph_indices, initalize_heap_from_graph_indices_and_distances, sparse_initalize_heap_from_graph_indices
  from pynndescent.rp_trees import make_forest, rptree_leaf_array, convert_tree_format, FlatTree, denumbaify_tree, renumbaify_tree, select_side, sparse_select_side, score_linked_tree
label_desc: Use image node_id 7 for calling a global function with example usage: process_candidates(data, dist, current_graph, new_candidate_neighbors, old_candidate_neighbors, n_blocks, block_size, n_threads) and returns: c