code: def _softmax_cross_entropy(p_trues, p_preds):
      def per_list_loss(y_true, y_pred):
          return sum(
              -y_t * math.log(1e-20 + y_p)
              for y_t, y_p in zip(y_true, y_pred)
          )
  
      return sum(
          per_list_loss(p_true, p_pred)
          for p_true, p_pred in zip(p_trues, p_preds)
      )
  
imports: import math
  import tensorflow
  from tensorflow_ranking.python import losses
  from tensorflow_ranking.python import losses_impl
label_desc: Use image node_id 8 for calling a global function with example usage: _softmax_cross_entropy(p_trues, p_preds) and returns: sum, sum