The TextVectorization layer, correct? So between the Input layer and the Embedding layer, I add the text vectorization layer like this:
... 
vectorize_layer = TextVectorization(
   max_tokens=vocab_size, # number of words (len(word_index) + 1)
   output_mode='int', # assuming 'int' because labels are 0,1,2
   output_sequence_length=MAXLEN # same as tokenizer
)(inp)
x = vectorize_layer
...

and this is how I set up my tokenizer:
tokenizer = text.Tokenizer() 
tokenizer.fit_on_texts(text)
text_tokenized = tokenizer.texts_to_sequences(text)
X_text = sequence.pad_sequences(text_tokenized, maxlen=MAXLEN)

How can the padding sequence be included in the layer or is there even a need? There’s a pad_to_max_tokens but I don’t think it works for ‘int’ output_mode.
Also, if I add that layer, do I have to tokenize, vectorize, etc. the text I want to predict or can I just pass the string and it does it through the layer?
EDIT: I pickled the tokenizer object then load it back before prediction, and it worked. Still would prefer a more convenient method.