code: def sparse_alternative_cosine(ind1, data1, ind2, data2):
      _, aux_data = sparse_mul(ind1, data1, ind2, data2)
      result = 0.0
      norm_x = norm(data1)
      norm_y = norm(data2)
      dim = len(aux_data)
      for i in range(dim):
          result += aux_data[i]
      if norm_x == 0.0 and norm_y == 0.0:
          return 0.0
      elif norm_x == 0.0 or norm_y == 0.0:
          return FLOAT32_MAX
      elif result <= 0.0:
          return FLOAT32_MAX
      else:
          result = (norm_x * 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 30 for calling a global function with example usage: sparse_alternative_cosine(ind1, data1, ind2, data2) and returns: int, FLOAT32_MAX, FLOAT32_MAX, np