code: def hellinger(x, y):
      result = 0.0
      l1_norm_x = 0.0
      l1_norm_y = 0.0
  
      for i in range(x.shape[0]):
          result += np.sqrt(x[i] * y[i])
          l1_norm_x += x[i]
          l1_norm_y += y[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 1.0
      else:
          return np.sqrt(1 - result / np.sqrt(l1_norm_x * l1_norm_y))
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 37 for calling a global function with example usage: hellinger(x, y) and returns: int, int, np