code: def _load_custom_objects(
      custom_objects: List[Tuple[str, str]]
  ) -> Dict[str, Type[Any]]:
      """Loads custom metric options."""
      loaded_custom_objects = {}
      for module_name, class_name in custom_objects:
          module = importlib.import_module(module_name)
          loaded_custom_objects[class_name] = getattr(
              module, class_name
          )
      return loaded_custom_objects
  
imports: import collections
  import importlib
  import itertools
  from typing import Any, Dict, Iterable, List, Optional, Type, Tuple, Union
  import apache_beam
  import numpy
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.metrics import binary_confusion_matrices
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.metrics import tf_metric_accumulators
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.utils import model_util
label_desc: Use image node_id 10 for calling a global function with example usage: _load_custom_objects(custom_objects) and returns: loaded_custom_objects