code: def sparse_manhattan(ind1, data1, ind2, data2):
      aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2)
      result = 0.0
      for i in range(aux_data.shape[0]):
          result += np.abs(aux_data[i])
      return result
  
imports: from __future__ import print_function
  import locale
  import numba
  import numpy
  from umap.utils import norm
label_desc: Use image node_id 10 for calling a global function with example usage: sparse_manhattan(ind1, data1, ind2, data2) and returns: result