def METHOD_NAME(tmp_path):
    train_x = test_utils.generate_data(num_instances=320, shape=(32, 32))
    train_y = test_utils.generate_one_hot_labels(
        num_instances=320, num_classes=10
    )
    clf = ak.ImageClassifier(
        directory=tmp_path,
        max_trials=2,
        seed=test_utils.SEED,
        distribution_strategy=tf.distribute.MirroredStrategy(),
    )
    clf.fit(train_x, train_y, epochs=1, validation_split=0.2)
    keras_model = clf.export_model()
    clf.evaluate(train_x, train_y)
    assert clf.predict(train_x).shape == (len(train_x), 10)
    assert isinstance(keras_model, keras.Model)