import tensorflow as tf

            def metric_fn(per_example_loss, label_ids, logits):
                predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
                precision = tf_metrics.precision(label_ids,predictions,11,[1,2,4,5,6,7,8,9],average="macro")
                recall = tf_metrics.recall(label_ids,predictions,11,[1,2,4,5,6,7,8,9],average="macro")
                f = tf_metrics.f1(label_ids,predictions,11,[1,2,4,5,6,7,8,9],average="macro")
                loss = tf.metrics.mean(per_example_loss)
                return {
                    "eval_precision":precision,
                    "eval_recall":recall,
                    "eval_f": f,
