code: def get(identifier):
      """Retrieves a Keras metric as a `function`/`Metric` class instance.
  
      The `identifier` may be the string name of a metric function or class.
  
      >>> metric = metrics.get("categorical_crossentropy")
      >>> type(metric)
      <class 'function'>
      >>> metric = metrics.get("CategoricalCrossentropy")
      >>> type(metric)
      <class '...metrics.CategoricalCrossentropy'>
  
      You can also specify `config` of the metric to this function by passing dict
      containing `class_name` and `config` as an identifier. Also note that the
      `class_name` must map to a `Metric` class
  
      >>> identifier = {"class_name": "CategoricalCrossentropy",
      ...               "config": {"from_logits": True}}
      >>> metric = metrics.get(identifier)
      >>> type(metric)
      <class '...metrics.CategoricalCrossentropy'>
  
      Args:
          identifier: A metric identifier. One of None or string name of a metric
              function/class or metric configuration dictionary or a metric
              function or a metric class instance
  
      Returns:
          A Keras metric as a `function`/ `Metric` class instance.
      """
      if identifier is None:
          return None
      if isinstance(identifier, dict):
          obj = deserialize(identifier)
      elif isinstance(identifier, str):
          obj = ALL_OBJECTS_DICT.get(identifier, None)
      else:
          obj = identifier
      if callable(obj):
          if inspect.isclass(obj):
              obj = obj()
          return obj
      else:
          raise ValueError(
              f"Could not interpret metric identifier: {identifier}"
          )
  
imports: import inspect
  from keras.api_export import keras_export
  from keras.metrics.accuracy_metrics import Accuracy
  from keras.metrics.accuracy_metrics import BinaryAccuracy
  from keras.metrics.accuracy_metrics import CategoricalAccuracy
  from keras.metrics.accuracy_metrics import SparseCategoricalAccuracy
  from keras.metrics.accuracy_metrics import SparseTopKCategoricalAccuracy
  from keras.metrics.accuracy_metrics import TopKCategoricalAccuracy
  from keras.metrics.confusion_metrics import AUC
  from keras.metrics.confusion_metrics import FalseNegatives
  from keras.metrics.confusion_metrics import FalsePositives
  from keras.metrics.confusion_metrics import Precision
  from keras.metrics.confusion_metrics import PrecisionAtRecall
  from keras.metrics.confusion_metrics import Recall
  from keras.metrics.confusion_metrics import RecallAtPrecision
  from keras.metrics.confusion_metrics import SensitivityAtSpecificity
  from keras.metrics.confusion_metrics import SpecificityAtSensitivity
  from keras.metrics.confusion_metrics import TrueNegatives
  from keras.metrics.confusion_metrics import TruePositives
  from keras.metrics.f_score_metrics import F1Score
  from keras.metrics.f_score_metrics import FBetaScore
  from keras.metrics.hinge_metrics import CategoricalHinge
  from keras.metrics.hinge_metrics import Hinge
  from keras.metrics.hinge_metrics import SquaredHinge
  from keras.metrics.iou_metrics import BinaryIoU
  from keras.metrics.iou_metrics import IoU
  from keras.metrics.iou_metrics import MeanIoU
  from keras.metrics.iou_metrics import OneHotIoU
  from keras.metrics.iou_metrics import OneHotMeanIoU
  from keras.metrics.metric import Metric
  from keras.metrics.probabilistic_metrics import BinaryCrossentropy
  from keras.metrics.probabilistic_metrics import CategoricalCrossentropy
  from keras.metrics.probabilistic_metrics import KLDivergence
  from keras.metrics.probabilistic_metrics import Poisson
  from keras.metrics.probabilistic_metrics import SparseCategoricalCrossentropy
  from keras.metrics.reduction_metrics import Mean
  from keras.metrics.reduction_metrics import MeanMetricWrapper
  from keras.metrics.reduction_metrics import Sum
  from keras.metrics.regression_metrics import CosineSimilarity
  from keras.metrics.regression_metrics import LogCoshError
  from keras.metrics.regression_metrics import MeanAbsoluteError
  from keras.metrics.regression_metrics import MeanAbsolutePercentageError
  from keras.metrics.regression_metrics import MeanSquaredError
  from keras.metrics.regression_metrics import MeanSquaredLogarithmicError
  from keras.metrics.regression_metrics import R2Score
  from keras.metrics.regression_metrics import RootMeanSquaredError
  from keras.saving import serialization_lib
  from keras.utils.naming import to_snake_case
label_desc: Use image node_id 3 for calling a global function with example usage: get(identifier) and returns: None, obj