code: def _serialize_tf_loss(
      loss: tf.keras.losses.Loss,
  ) -> config_pb2.MetricConfig:
      """Serializes TF loss."""
      cfg = metric_util.serialize_loss(loss, use_legacy_format=True)
      return config_pb2.MetricConfig(
          class_name=cfg["class_name"],
          module=loss.__class__.__module__,
          config=json.dumps(cfg["config"], sort_keys=True),
      )
  
imports: import collections
  import importlib
  import json
  import re
  from typing import Any, Dict, FrozenSet, Iterable, Iterator, List, NamedTuple, Optional, Tuple, Type, Union
  import tensorflow
  from tensorflow_model_analysis.metrics import aggregation
  from tensorflow_model_analysis.metrics import binary_confusion_matrices
  from tensorflow_model_analysis.metrics import calibration
  from tensorflow_model_analysis.metrics import calibration_plot
  from tensorflow_model_analysis.metrics import confusion_matrix_metrics
  from tensorflow_model_analysis.metrics import confusion_matrix_plot
  from tensorflow_model_analysis.metrics import example_count
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.metrics import multi_class_confusion_matrix_plot
  from tensorflow_model_analysis.metrics import tf_metric_wrapper
  from tensorflow_model_analysis.metrics import weighted_example_count
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.slicer import slicer_lib
  from tensorflow_model_analysis.utils import model_util
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 29 for calling a global function with example usage: _serialize_tf_loss(loss) and returns: config_pb2