text: import tensorflow as tf
  
  
  def kld(y_true, y_pred, eps=1e-7):
      """This function computes the Kullback-Leibler divergence between ground
         truth saliency maps and their predictions. Values are first divided by
         their sum for each image to yield a distribution that adds to 1.
  
      Args:
          y_true (tensor, float32): A 4d tensor that holds the ground truth
                                    saliency maps with values between 0 and 255.
          y_pred (tensor, float32): A 4d tensor that holds the predicted saliency
                                    maps with values between 0 and 1.
          eps (scalar, float, optional): A small factor to avoid numerical
                                         instabilities. Defaults to 1e-7.
  
      Returns:
          tensor, float32: A 0D tensor that holds the averaged error.
      """
  
      sum_per_image = tf.reduce_sum(y_true, axis=(1, 2, 3), keep_dims=True)
      y_true /= eps + sum_per_image
  
      sum_per_image = tf.reduce_sum(y_pred, axis=(1, 2, 3), keep_dims=True)
      y_pred /= eps + sum_per_image
  
      loss = y_true * tf.log(eps + y_true / (eps + y_pred))
      loss = tf.reduce_mean(tf.reduce_sum(loss, axis=(1, 2, 3)))
  
      return loss
  