What I currently have and trying to do:
When I receive a request from a client to the model in tensorflow-serving,  I first need to process them using 13 regexes, then pass that text through tf.keras.preprocessing.text.Tokenizer 1 to convert them to numbers(or token), and then pass them to tf.keras.preprocessing.sequence.pad_sequences to add 0s (for the sentences whose lenght doesn’t match the input that the model expects) at the end of each array(for a batch of inputs), then this(a single sentence or a batch of sentences as tokens) will be fed to a tf.keras model to get some probabilities as outputs. And I then need to map these probabilites(different thresholds for different units) to texts and return it to the client.

What problems am I currently facing trying to accomplish above:
While trying to put together all that to be able to serve the model using tensorflow-serving, I learned that some parts can be converted to tensorflow functions, but not all of it.

regexes: I still couldn’t figure out where and how to put my regexes to be able to manipulate the text.
tokenizer: I learned from some blogs and SO questions, that tf.lookup.StaticHashTable can be used for this purpose.
pad_sequences: no help with this too.
post-processing: I could find very little information to do this.

I read the beginner and advanced blogs on tensorflow-transform tutorials page, but either of them mentioned how to link those tft functions to the tf-keras model, while saving it. And I could also find some information about adding pre-processing for serving, but all of them involved tensorflow code and some workarounds, but they didn’t cover what I am trying to achieve even indirectly.
I can provide more information as required.
How do I add these steps to the graph, while saving the model?