code: def init_rp_tree(data, dist, current_graph, leaf_array):
      n_leaves = leaf_array.shape[0]
      block_size = 65536
      n_blocks = n_leaves // block_size
  
      for i in range(n_blocks + 1):
          block_start = i * block_size
          block_end = min(n_leaves, (i + 1) * block_size)
  
          leaf_block = leaf_array[block_start:block_end]
          dist_thresholds = current_graph[1][:, 0]
  
          updates = generate_leaf_updates(
              leaf_block, dist_thresholds, data, dist
          )
  
          for j in range(len(updates)):
              for k in range(len(updates[j])):
                  p, q, d = updates[j][k]
  
                  if p == -1 or q == -1:
                      continue
  
                  checked_flagged_heap_push(
                      current_graph[1][p],
                      current_graph[0][p],
                      current_graph[2][p],
                      d,
                      q,
                      np.uint8(1),
                  )
                  checked_flagged_heap_push(
                      current_graph[1][q],
                      current_graph[0][q],
                      current_graph[2][q],
                      d,
                      p,
                      np.uint8(1),
                  )
  
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 3 for calling a global function with example usage: init_rp_tree(data, dist, current_graph, leaf_array) without return types