code: def sparse_correlation(ind1, data1, ind2, data2, n_features):
      mu_x = 0.0
      mu_y = 0.0
      dot_product = 0.0
  
      if ind1.shape[0] == 0 and ind2.shape[0] == 0:
          return 0.0
      elif ind1.shape[0] == 0 or ind2.shape[0] == 0:
          return 1.0
  
      for i in range(data1.shape[0]):
          mu_x += data1[i]
      for i in range(data2.shape[0]):
          mu_y += data2[i]
  
      mu_x /= n_features
      mu_y /= n_features
  
      shifted_data1 = np.empty(data1.shape[0], dtype=np.float32)
      shifted_data2 = np.empty(data2.shape[0], dtype=np.float32)
  
      for i in range(data1.shape[0]):
          shifted_data1[i] = data1[i] - mu_x
      for i in range(data2.shape[0]):
          shifted_data2[i] = data2[i] - mu_y
  
      norm1 = np.sqrt(
          (norm(shifted_data1) ** 2)
          + (n_features - ind1.shape[0]) * (mu_x**2)
      )
      norm2 = np.sqrt(
          (norm(shifted_data2) ** 2)
          + (n_features - ind2.shape[0]) * (mu_y**2)
      )
  
      dot_prod_inds, dot_prod_data = sparse_mul(
          ind1, shifted_data1, ind2, shifted_data2
      )
  
      common_indices = set(dot_prod_inds)
  
      for i in range(dot_prod_data.shape[0]):
          dot_product += dot_prod_data[i]
  
      for i in range(ind1.shape[0]):
          if ind1[i] not in common_indices:
              dot_product -= shifted_data1[i] * (mu_y)
  
      for i in range(ind2.shape[0]):
          if ind2[i] not in common_indices:
              dot_product -= shifted_data2[i] * (mu_x)
  
      all_indices = arr_union(ind1, ind2)
      dot_product += mu_x * mu_y * (n_features - all_indices.shape[0])
  
      if norm1 == 0.0 and norm2 == 0.0:
          return 0.0
      elif dot_product == 0.0:
          return 1.0
      else:
          return 1.0 - (dot_product / (norm1 * norm2))
  
imports: from __future__ import print_function
  import locale
  import numba
  import numpy
  from umap.utils import norm
label_desc: Use image node_id 26 for calling a global function with example usage: sparse_correlation(ind1, data1, ind2, data2, n_features) and returns: int, int, int, int, unknown