code: def _deserialize_tfma_metric(
      metric_config: config_pb2.MetricConfig,
      custom_objects: Dict[str, Type[metric_types.Metric]],
  ) -> metric_types.Metric:
      """Deserializes a tfma.metrics metric."""
      with tf.keras.utils.custom_object_scope(custom_objects):
          return metric_util.deserialize_keras_object(
              {
                  "class_name": metric_config.class_name,
                  "config": _metric_config(metric_config.config),
              }
          )
  
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 33 for calling a global function with example usage: _deserialize_tfma_metric(metric_config, custom_objects) and returns: metric_util