tf.nn.compute_average_loss

tf.nn.compute_average_loss(
    per_example_loss, sample_weight=None, global_batch_size=None
)

with strategy.scope():
  def compute_loss(labels, predictions, sample_weight=None):

    # If you are using a `Loss` class instead, set reduction to `NONE` so that
    # we can do the reduction afterwards and divide by global batch size.
    per_example_loss = tf.keras.losses.sparse_categorical_crossentropy(
        labels, predictions)

    # Compute loss that is scaled by sample_weight and by global batch size.
    return tf.compute_average_loss(
        per_example_loss,
        sample_weight=sample_weight,
        global_batch_size=GLOBAL_BATCH_SIZE)