code: def sparse_rogers_tanimoto(ind1, data1, ind2, data2, n_features):
      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
  
      return (2.0 * num_not_equal) / (n_features + 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 25 for calling a global function with example usage: sparse_rogers_tanimoto(ind1, data1, ind2, data2, n_features) and returns: unknown