Is it possible to split a tensorflow dataset into train, validation AND test datasets when using image_dataset_from_directory?

Question

I am using tf.keras.utils.image_dataset_from_directory to load a dataset of 4575 images. While this function allows to split the data into two subsets (with the validation_split parameter), I want to split it into training, testing, and validation subsets.

I have tried using dataset.skip() and dataset.take() to further split one of the resulting subsets, but these functions return a SkipDataset and a TakeDataset respectively (by the way, contrary to the documentation, where it is claimed that these functions return a Dataset). This leads to problems when fitting the model - the metrics calculated on validation sets (val_loss, val_accuracy) disappear from model history.

So, my question is: is there a way to split a Dataset into three subsets for training, validation and testing, so that all three subsets are also Dataset objects?

Code used to load the data

def load_data_tf(data_path: str, img_shape=(256,256), batch_size: int=8):
    train_ds = tf.keras.utils.image_dataset_from_directory(
        data_path,
        validation_split=0.2,
        subset="training",
        label_mode='categorical',
        seed=123,
        image_size=img_shape,
        batch_size=batch_size)
    val_ds = tf.keras.utils.image_dataset_from_directory(
        data_path,
        validation_split=0.3,
        subset="validation",
        label_mode='categorical',
        seed=123,
        image_size=img_shape,
        batch_size=batch_size)
    return train_ds, val_ds

train_dataset, test_val_ds = load_data_tf('data_folder', img_shape = (256,256), batch_size=8)
test_dataset = test_val_ds.take(686)
val_dataset = test_val_ds.skip(686)

Model compilation and fitting

model.compile(optimizer='sgd',
              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False),
              metrics=['accuracy'])
history = model.fit(train_dataset, epochs=50, validation_data=val_dataset, verbose=1)

When using a normal Dataset, val_accuracy and val_loss are present in the history of the model:

But when using a SkipDataset, they are not: