code: def symmetric_kl_grad(x, y, z=1e-11):  # pragma: no cover
      """
      symmetrized KL divergence and its gradient
  
      """
      n = x.shape[0]
      x_sum = 0.0
      y_sum = 0.0
      kl1 = 0.0
      kl2 = 0.0
  
      for i in range(n):
          x[i] += z
          x_sum += x[i]
          y[i] += z
          y_sum += y[i]
  
      for i in range(n):
          x[i] /= x_sum
          y[i] /= y_sum
  
      for i in range(n):
          kl1 += x[i] * np.log(x[i] / y[i])
          kl2 += y[i] * np.log(y[i] / x[i])
  
      dist = (kl1 + kl2) / 2
      grad = (np.log(y / x) - (x / y) + 1) / 2
  
      return dist, grad
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 44 for calling a global function with example usage: symmetric_kl_grad(x, y, z) and returns: dist,  grad