code: def sparse_cosine(ind1, data1, ind2, data2):
      _, aux_data = sparse_mul(ind1, data1, ind2, data2)
      result = 0.0
      norm1 = norm(data1)
      norm2 = norm(data2)
  
      for val in aux_data:
          result += val
  
      if norm1 == 0.0 and norm2 == 0.0:
          return 0.0
      elif norm1 == 0.0 or norm2 == 0.0:
          return 1.0
      else:
          return 1.0 - (result / (norm1 * norm2))
  
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 29 for calling a global function with example usage: sparse_cosine(ind1, data1, ind2, data2) and returns: int, int, unknown