code: def sparse_alternative_hellinger(ind1, data1, ind2, data2):
      aux_inds, aux_data = sparse_mul(ind1, data1, ind2, data2)
      result = 0.0
      l1_norm_x = np.sum(data1)
      l1_norm_y = np.sum(data2)
      dim = len(aux_data)
  
      for i in range(dim):
          result += np.sqrt(aux_data[i])
  
      if l1_norm_x == 0 and l1_norm_y == 0:
          return 0.0
      elif l1_norm_x == 0 or l1_norm_y == 0:
          return FLOAT32_MAX
      elif result <= 0:
          return FLOAT32_MAX
      else:
          result = np.sqrt(l1_norm_x * l1_norm_y) / result
          return np.log2(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 36 for calling a global function with example usage: sparse_alternative_hellinger(ind1, data1, ind2, data2) and returns: int, FLOAT32_MAX, FLOAT32_MAX, np