code: def create_metrics():
      return [
          tf.keras.metrics.CategoricalAccuracy(),
          tf.keras.metrics.CategoricalCrossentropy(),
          tf.keras.metrics.TruePositives(),
          tf.keras.metrics.TrueNegatives(),
          tf.keras.metrics.FalsePositives(),
          tf.keras.metrics.FalseNegatives(),
          tf.keras.metrics.Precision(),
          tf.keras.metrics.Precision(
              name="precision_homref", class_id=0
          ),
          tf.keras.metrics.Precision(name="precision_het", class_id=1),
          tf.keras.metrics.Precision(
              name="precision_homalt", class_id=2
          ),
          tf.keras.metrics.Recall(),
          tf.keras.metrics.Recall(name="recall_homref", class_id=0),
          tf.keras.metrics.Recall(name="recall_het", class_id=1),
          tf.keras.metrics.Recall(name="recall_homalt", class_id=2),
          tfa.metrics.F1Score(
              num_classes=3, average="weighted", name="f1_weighted"
          ),
          tfa.metrics.F1Score(
              num_classes=3, average="micro", name="f1_micro"
          ),
          tfa.metrics.F1Score(
              num_classes=3, average="macro", name="f1_macro"
          ),
          F1ScorePerClass(
              num_classes=3, target_class=0, name="f1_homref"
          ),
          F1ScorePerClass(num_classes=3, target_class=1, name="f1_het"),
          F1ScorePerClass(
              num_classes=3, target_class=2, name="f1_homalt"
          ),
          # Leave mean loss as the last metric as it is updated differently.
          tf.keras.metrics.Mean(name="loss"),
      ]
  
imports: import os
  import tempfile
  from typing import Callable, Optional, Tuple, Type, Union
  from absl import logging
  import ml_collections
  import numpy
  import tensorflow
  import tensorflow_addons
  from deepvariant import dv_constants
  from deepvariant import dv_utils
label_desc: Use image node_id 8 for calling a global function with example usage: create_metrics() and returns: list