code: def _private_tf_loss(
      loss: tf.keras.losses.Loss,
  ) -> tf.keras.losses.Loss:
      """Creates a private version of given loss."""
      cfg = metric_util.serialize_loss(loss)
      if not cfg["config"]["name"].startswith("_"):
          cfg["config"]["name"] = "_" + cfg["config"]["name"]
      with tf.keras.utils.custom_object_scope(
          {loss.__class__.__name__: loss.__class__}
      ):
          return metric_util.deserialize_loss(
              cfg, use_legacy_format=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 31 for calling a global function with example usage: _private_tf_loss(loss) and returns: metric_util