Meta Learning: how to train a model with Support Set and Query Set

I've just started to learn Meta Learning reading the book Hands-On Meta Learning with Python.

I think I know the answer for my question, but I'm a little confuse about how to implement the algorithm with Keras.

This piece of code is from an example that uses U-NET:

from sklearn.model_selection import train_test_split

# Split train and valid
X_train, X_valid, y_train, y_valid = train_test_split(train_data, test_data, test_size=0.1, random_state=42)

results = model.fit(X_train, y_train, batch_size=32, epochs=50,\
                    validation_data=(X_valid, y_valid))

My problem is with the fit method.

Reading the book I read:

...train on the support set and test on the query set...

So here, in a meta learning model, the train_data is the Support Set, and the test_data is the query set, isn't it?