def METHOD_NAME(_):
    keras.mixed_precision.set_dtype_policy(FLAGS.mixed_precision_policy)
    logging.info(
        "Benchmarking configs...\n"
        "=========================\n"
        f"MODEL: {FLAGS.model}\n"
        f"TASK: image classification/dogs-vs-cats \n"
        f"BATCH_SIZE: {FLAGS.batch_size}\n"
        f"EPOCHS: {FLAGS.epochs}\n"
        "=========================\n"
    )
    # Load datasets.
    train_ds, validation_ds = load_data()
    # Load the model.
    classifier = load_model()
    lr = keras.optimizers.schedules.PolynomialDecay(
        5e-4,
        decay_steps=train_ds.cardinality() * FLAGS.epochs,
        end_learning_rate=0.0,
    )
    optimizer = keras.optimizers.Adam(lr)
    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    benchmark_metrics_callback = BenchmarkMetricsCallback(
        start_batch=1,
        stop_batch=train_ds.cardinality().numpy() - 1,
    )
    classifier.compile(
        optimizer=optimizer,
        loss=loss,
        metrics=["sparse_categorical_accuracy"],
    )
    # Start training.
    logging.info("Starting Training...")
    st = time.time()
    history = classifier.fit(
        train_ds,
        validation_data=validation_ds,
        epochs=FLAGS.epochs,
        callbacks=[benchmark_metrics_callback],
    )
    wall_time = time.time() - st
    validation_accuracy = history.history["val_sparse_categorical_accuracy"][-1]
    examples_per_second = (
        np.mean(np.array(benchmark_metrics_callback.state["throughput"]))
        * FLAGS.batch_size
    )
    logging.info("Training Finished!")
    logging.info(f"Wall Time: {wall_time:.4f} seconds.")
    logging.info(f"Validation Accuracy: {validation_accuracy:.4f}")
    logging.info(f"examples_per_second: {examples_per_second:.4f}")