code: def _deserialize_tf_loss(
      metric_config: config_pb2.MetricConfig,
      custom_objects: Dict[str, Type[tf.keras.losses.Loss]],
  ) -> tf.keras.losses.Loss:
      """Deserializes a tf.keras.loss metric."""
      cls_name, cfg = _tf_class_and_config(metric_config)
      with tf.keras.utils.custom_object_scope(custom_objects):
          return metric_util.deserialize_loss(
              {"class_name": cls_name, "config": 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 30 for calling a global function with example usage: _deserialize_tf_loss(metric_config, custom_objects) and returns: metric_util