code: def sparse_dice(ind1, data1, ind2, data2):
      num_true_true = fast_intersection_size(ind1, ind2)
      num_non_zero = ind1.shape[0] + ind2.shape[0] - num_true_true
      num_not_equal = num_non_zero - num_true_true
  
      if num_not_equal == 0.0:
          return 0.0
      else:
          return num_not_equal / (2.0 * num_true_true + num_not_equal)
  
imports: from __future__ import print_function
  import locale
  import numpy
  import numba
  from pynndescent.utils import norm, tau_rand
  from pynndescent.distances import kantorovich, jensen_shannon_divergence, symmetric_kl_divergence
label_desc: Use image node_id 23 for calling a global function with example usage: sparse_dice(ind1, data1, ind2, data2) and returns: int, unknown