code: def log_metrics(model_desc, eval_metrics):
      """Logs evaluation metrics at `logging.INFO` level.
  
      Args:
        model_desc: A description of the model.
        eval_metrics: A dictionary mapping metric names to corresponding values. It
          must contain the loss and accuracy metrics.
      """
      logging.info("\n")
      logging.info(
          "Eval accuracy for %s: %s",
          model_desc,
          eval_metrics["accuracy"],
      )
      logging.info(
          "Eval loss for %s: %s", model_desc, eval_metrics["loss"]
      )
      if "graph_loss" in eval_metrics:
          logging.info(
              "Eval graph loss for %s: %s",
              model_desc,
              eval_metrics["graph_loss"],
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import threading
  from absl import app
  from absl import flags
  from absl import logging
  import attr
  import neural_structured_learning
  from research.carls import dynamic_embedding_config_pb2
  from research.carls import dynamic_embedding_neighbor_cache
  from research.carls import graph_regularization
  from research.carls import kbs_server_helper_pybind
  import tensorflow
  from google.protobuf import text_format
label_desc: Use image node_id 7 for calling a global function with example usage: log_metrics(model_desc, eval_metrics) without return types