code: def hellinger_grad(x, y):
      result = 0.0
      l1_norm_x = 0.0
      l1_norm_y = 0.0
  
      grad_term = np.empty(x.shape[0])
  
      for i in range(x.shape[0]):
          grad_term[i] = np.sqrt(x[i] * y[i])
          result += grad_term[i]
          l1_norm_x += x[i]
          l1_norm_y += y[i]
  
      if l1_norm_x == 0 and l1_norm_y == 0:
          dist = 0.0
          grad = np.zeros(x.shape)
      elif l1_norm_x == 0 or l1_norm_y == 0:
          dist = 1.0
          grad = np.zeros(x.shape)
      else:
          dist_denom = np.sqrt(l1_norm_x * l1_norm_y)
          dist = np.sqrt(1 - result / dist_denom)
          grad_denom = 2 * dist
          grad_numer_const = (l1_norm_y * result) / (
              2 * dist_denom**3
          )
  
          grad = (
              grad_numer_const - (y / grad_term * dist_denom)
          ) / grad_denom
  
      return dist, grad
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 38 for calling a global function with example usage: hellinger_grad(x, y) and returns: dist,  grad