code: def diversify(
      indices, distances, data, dist, rng_state, prune_probability=1.0
  ):
      for i in numba.prange(indices.shape[0]):
          new_indices = [indices[i, 0]]
          new_distances = [distances[i, 0]]
          for j in range(1, indices.shape[1]):
              if indices[i, j] < 0:
                  break
  
              flag = True
              for k in range(len(new_indices)):
                  c = new_indices[k]
  
                  d = dist(data[indices[i, j]], data[c])
                  if (
                      new_distances[k] > FLOAT32_EPS
                      and d < distances[i, j]
                  ):
                      if tau_rand(rng_state) < prune_probability:
                          flag = False
                          break
  
              if flag:
                  new_indices.append(indices[i, j])
                  new_distances.append(distances[i, j])
  
          for j in range(indices.shape[1]):
              if j < len(new_indices):
                  indices[i, j] = new_indices[j]
                  distances[i, j] = new_distances[j]
              else:
                  indices[i, j] = -1
                  distances[i, j] = np.inf
  
      return indices, distances
  
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 11 for calling a global function with example usage: diversify(indices, distances, data, dist, rng_state, prune_probability) and returns: indices,  distances