code: def init_from_neighbor_graph(heap, indices, distances):
      for p in range(indices.shape[0]):
          for k in range(indices.shape[1]):
              q = indices[p, k]
              d = distances[p, k]
              checked_flagged_heap_push(
                  heap[1][p], heap[0][p], heap[2][p], d, q, 0
              )
  
      return
  
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 5 for calling a global function with example usage: init_from_neighbor_graph(heap, indices, distances) without return types