code: def init_random(n_neighbors, data, heap, dist, rng_state):
      for i in range(data.shape[0]):
          if heap[0][i, 0] < 0.0:
              for j in range(n_neighbors - np.sum(heap[0][i] >= 0.0)):
                  idx = np.abs(tau_rand_int(rng_state)) % data.shape[0]
                  d = dist(data[idx], data[i])
                  checked_flagged_heap_push(
                      heap[1][i],
                      heap[0][i],
                      heap[2][i],
                      d,
                      idx,
                      np.uint8(1),
                  )
  
      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 4 for calling a global function with example usage: init_random(n_neighbors, data, heap, dist, rng_state) without return types