code: def _verify_and_update_sub_key(
      model_name: str,
      output_name: str,
      sub_key: metric_types.SubKey,
      metric: _TFMetricOrLoss,
  ):
      """Verifies the multi-class metric key matches settings used by the metric."""
      if hasattr(metric, _CLASS_ID_KEY) and metric.class_id is not None:
          if sub_key and sub_key.class_id != metric.class_id:
              raise ValueError(
                  "{} tf.keras.metric has class_id = {}, but the metric is being added "
                  "using sub_key = {}: model_name={}, output_name={}".format(
                      metric.name,
                      metric.class_id,
                      sub_key,
                      model_name,
                      output_name,
                  )
              )
          return metric_types.SubKey(class_id=metric.class_id)
      elif hasattr(metric, _TOP_K_KEY) and metric.top_k is not None:
          if sub_key and sub_key.top_k != metric.top_k:
              raise ValueError(
                  "{} tf.keras.metric has top_k = {}, but the metric is being added "
                  "using sub_key = {}: model_name={}, output_name={}".format(
                      metric.name,
                      metric.top_k,
                      sub_key,
                      model_name,
                      output_name,
                  )
              )
          return metric_types.SubKey(top_k=metric.top_k)
      else:
          return sub_key
  
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 5 for calling a global function with example usage: _verify_and_update_sub_key(model_name, output_name, sub_key, metric) and returns: metric_types, metric_types, sub_key