def METHOD_NAME(inputs):
    """The computation to run on each replica(GPU)."""
    features, labels = inputs
    with tf.GradientTape() as tape:
        pred = model(features, training=True)
        loss = keras.losses.binary_crossentropy(labels, pred)
        loss = tf.nn.compute_average_loss(loss)
    grads = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(
        list(zip(grads, model.trainable_variables))
    )
    actual_pred = tf.cast(tf.greater(pred, 0.5), tf.int64)
    accuracy.update_state(labels, actual_pred)