code: def symmetric_kl_divergence(predicted, actual):
      """Calculate symmetric KL-divergence over two classification tensors.
  
      Note that here the classifications do not form a probability distribution.
      They are, however normalized to 0..1 and calculating a KL-divergence over them
      gives reasonable numerical results.
  
      Shape of the two inputs must be the same at inference time but is otherwise
      unconstrained.
  
      Args:
        predicted: classification outputs from model
        actual: golden classification outputs
  
      Returns:
        Single scalar tensor with symmetric KL-divergence between predicted and
        actual.
      """
      epsilon = tf.constant(1e-7, dtype=tf.float32, name="epsilon")
      p = tf.math.maximum(predicted, epsilon)
      q = tf.math.maximum(actual, epsilon)
      kld_1 = tf.math.reduce_sum(
          tf.math.multiply(p, tf.math.log(tf.math.divide(p, q)))
      )
      kld_2 = tf.math.reduce_sum(
          tf.math.multiply(q, tf.math.log(tf.math.divide(q, p)))
      )
      return tf.add(kld_1, kld_2)
  
label_desc: Use image node_id 1 for calling a global function with example usage: symmetric_kl_divergence(predicted, actual) and returns: tf