How to use the deepface.dataset.Dataset function in deepface

def __init__(self, cl_path, dataset_path, image_size, batch_size, shuffle=True):
        self.dataset_path, self.image_size, self.batch_size, self.shuffle = [value for value in (dataset_path, image_size, batch_size, shuffle)] # use shuffle only with train, not with test
        
        self.class_labels = Dataset.get_class_labels(cl_path)

        # the number of classes present should 
        # be evaluated well in advance :(
        # if you run into some errors,
        # consider commenting the with block
        # and setting num_classes manually externally
        with tf.Session() as sess:
            self.num_classes = sess.run(tf.shape(self.class_labels)[0])
        self.data = self.get_dataset()

def get_train_test_dataset(cl_path, dataset_path, image_size, batch_size):
    train_path = '/train'
    test_path = '/test'
    train, test = [
        Dataset(cl_path, dataset_path + curr_path, image_size, batch_size, training)
        for curr_path, training in zip((train_path, test_path), (True, False))
    ]
    return train, test

def get_image_and_class(self, image, classl):
        classl = tf.math.equal(self.class_labels, classl)
        classl = tf.cast(classl, tf.int32)
        classl = tf.argmax(classl, axis=-1)
        classl = tf.one_hot(classl, self.num_classes)

        image = tf.image.decode_jpeg(image, channels=3)
        image = tf.image.resize_image_with_pad(image, self.image_size[0], self.image_size[1])
        image = tf.cast(image, tf.float32)
        image = Dataset.preprocess_image(image)

        return image, classl