code: def sparse_kantorovich(
      ind1, data1, ind2, data2, ground_metric=dummy_ground_metric
  ):
      cost_matrix = np.empty((ind1.shape[0], ind2.shape[0]))
      for i in range(ind1.shape[0]):
          for j in range(ind2.shape[0]):
              cost_matrix[i, j] = ground_metric(ind1[i], ind2[j])
  
      return kantorovich(data1, data2, cost_matrix)
  
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 40 for calling a global function with example usage: sparse_kantorovich(ind1, data1, ind2, data2, ground_metric) and returns: kantorovich