code: def sparse_dot_product(ind1, data1, ind2, data2):
      dim1 = ind1.shape[0]
      dim2 = ind2.shape[0]
  
      result = 0.0
  
      i1 = 0
      i2 = 0
      j1 = ind1[i1]
      j2 = ind2[i2]
  
      # pass through both index lists
      while True:
          if j1 == j2:
              val = data1[i1] * data2[i2]
              result += val
              i1 += 1
              if i1 >= dim1:
                  return result
              j1 = ind1[i1]
              i2 += 1
              if i2 >= dim2:
                  return result
              j2 = ind2[i2]
          elif j1 < j2:
              i1 += 1
              if i1 >= dim1:
                  return result
              j1 = ind1[i1]
          else:
              i2 += 1
              if i2 >= dim2:
                  return result
              j2 = ind2[i2]
  
      return result
  
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 9 for calling a global function with example usage: sparse_dot_product(ind1, data1, ind2, data2) and returns: result, result, result, result, result