code: def score_tree(tree, neighbor_indices, data, rng_state):
      result = 0.0
      for i in numba.prange(neighbor_indices.shape[0]):
          leaf_indices = search_flat_tree(
              data[i],
              tree.hyperplanes,
              tree.offsets,
              tree.children,
              tree.indices,
              rng_state,
          )
          intersection = arr_intersect(
              neighbor_indices[i], leaf_indices
          )
          result += numba.float32(intersection.shape[0] > 1)
      return result / numba.float32(neighbor_indices.shape[0])
  
imports: from warnings import warn
  import locale
  import numpy
  import numba
  import scipy.sparse
  from pynndescent.sparse import sparse_mul, sparse_diff, sparse_sum, arr_intersect, sparse_dot_product
  from pynndescent.utils import tau_rand_int, norm
  import joblib
  from collections import namedtuple
label_desc: Use image node_id 25 for calling a global function with example usage: score_tree(tree, neighbor_indices, data, rng_state) and returns: unknown