code: def generate_graph_updates(
      new_candidate_block,
      old_candidate_block,
      dist_thresholds,
      data,
      dist,
  ):
      block_size = new_candidate_block.shape[0]
      updates = [[(-1, -1, np.inf)] for i in range(block_size)]
      max_candidates = new_candidate_block.shape[1]
  
      for i in numba.prange(block_size):
          for j in range(max_candidates):
              p = int(new_candidate_block[i, j])
              if p < 0:
                  continue
  
              for k in range(j, max_candidates):
                  q = int(new_candidate_block[i, k])
                  if q < 0:
                      continue
  
                  d = dist(data[p], data[q])
                  if d <= dist_thresholds[p] or d <= dist_thresholds[q]:
                      updates[i].append((p, q, d))
  
              for k in range(max_candidates):
                  q = int(old_candidate_block[i, k])
                  if q < 0:
                      continue
  
                  d = dist(data[p], data[q])
                  if d <= dist_thresholds[p] or d <= dist_thresholds[q]:
                      updates[i].append((p, q, d))
  
      return updates
  
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 6 for calling a global function with example usage: generate_graph_updates(new_candidate_block, old_candidate_block, dist_thresholds, data, dist) and returns: updates