code: def _get_model(metric, num_output):
      # Test API comptibility with tf.keras Model
      model = tf.keras.Sequential()
      model.add(tf.keras.layers.Dense(64, activation="relu"))
      model.add(tf.keras.layers.Dense(num_output, activation="softmax"))
      model.compile(
          optimizer="adam",
          loss="categorical_crossentropy",
          metrics=["acc", metric],
      )
  
      data = np.random.random((10, 3))
      labels = np.random.random((10, num_output))
      model.fit(data, labels, epochs=1, batch_size=5, verbose=0)
  
file_path: addons/tensorflow_addons/metrics/utils.py
imports: import numpy
  import tensorflow
  from tensorflow_addons.utils.types import AcceptableDTypes
  from typeguard import typechecked
  from typing import Optional, Callable
label_desc: Use image node_id 1 for calling a global function with example usage: _get_model(metric, num_output) without return types