code: def sparse_russellrao(ind1, data1, ind2, data2, n_features):
      if ind1.shape[0] == ind2.shape[0] and np.all(ind1 == ind2):
          return 0.0
  
      num_true_true = fast_intersection_size(ind1, ind2)
  
      if num_true_true == np.sum(
          data1 != 0
      ) and num_true_true == np.sum(data2 != 0):
          return 0.0
      else:
          return float(n_features - num_true_true) / (n_features)
  
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 26 for calling a global function with example usage: sparse_russellrao(ind1, data1, ind2, data2, n_features) and returns: int, int, unknown