code: def sparse_bray_curtis(ind1, data1, ind2, data2):  # pragma: no cover
      _, denom_data = sparse_sum(ind1, data1, ind2, data2)
      denom_data = np.abs(denom_data)
  
      if denom_data.shape[0] == 0:
          return 0.0
  
      denominator = np.sum(denom_data)
  
      if denominator == 0.0:
          return 0.0
  
      _, numer_data = sparse_diff(ind1, data1, ind2, data2)
      numer_data = np.abs(numer_data)
  
      numerator = np.sum(numer_data)
  
      return float(numerator) / denominator
  
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 18 for calling a global function with example usage: sparse_bray_curtis(ind1, data1, ind2, data2) and returns: unknown, int, int