code: def compile_bert_classifier(
      model: tf.keras.Model,
      loss: tf.keras.losses.Loss = tf.keras.losses.SparseCategoricalCrossentropy(
          from_logits=True
      ),
      learning_rate: float = 2e-5,
      metrics: Optional[
          List[Union[str, tf.keras.metrics.Metric]]
      ] = None,
  ):
      """Compile the BERT classifier using suggested parameters.
  
      Args:
        model: A keras model. Most likely the output of build_bert_classifier.
        loss: tf.keras.losses. The suggested loss function expects integer labels
          (e.g. 0, 1, 2). If the labels are one-hot encoded, consider using
          tf.keras.lossesCategoricalCrossEntropy with from_logits set to true.
        learning_rate: Suggested learning rate to be used in
          tf.keras.optimizer.Adam. The three suggested learning_rates for
          fine-tuning are [2e-5, 3e-5, 5e-5].
        metrics: Default None will use ['sparse_categorical_accuracy']. An array of
          strings or tf.keras.metrics.
  
      Returns:
        None.
      """
      if metrics is None:
          metrics = ["sparse_categorical_accuracy"]
  
      model.compile(
          optimizer=tf.keras.optimizers.Adam(learning_rate),
          loss=loss,
          metrics=metrics,
      )
  
imports: from typing import Optional, List, Union
  import tensorflow
  import tensorflow.keras
label_desc: Use image node_id 2 for calling a global function with example usage: compile_bert_classifier(model, loss, learning_rate, metrics) without return types