code: def sparse_hamming(ind1, data1, ind2, data2, n_features):
      num_not_equal = sparse_diff(ind1, data1, ind2, data2)[0].shape[0]
      return float(num_not_equal) / n_features
  
imports: from __future__ import print_function
  import locale
  import numba
  import numpy
  from umap.utils import norm
label_desc: Use image node_id 13 for calling a global function with example usage: sparse_hamming(ind1, data1, ind2, data2, n_features) and returns: unknown