code: def generate_leaf_updates(leaf_block, dist_thresholds, data, dist):
      updates = [[(-1, -1, np.inf)] for i in range(leaf_block.shape[0])]
  
      for n in numba.prange(leaf_block.shape[0]):
          for i in range(leaf_block.shape[1]):
              p = leaf_block[n, i]
              if p < 0:
                  break
  
              for j in range(i + 1, leaf_block.shape[1]):
                  q = leaf_block[n, j]
                  if q < 0:
                      break
  
                  d = dist(data[p], data[q])
                  if d < dist_thresholds[p] or d < dist_thresholds[q]:
                      updates[n].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 2 for calling a global function with example usage: generate_leaf_updates(leaf_block, dist_thresholds, data, dist) and returns: updates