TfidfVectorizer how to override `build_preprocessor`

I'm extending the standard TfidfVectorizer to add an additional mandatory preprocessing step. Looking at the source code for build_preprocessor, their code has a "branching" logic, but I want an additional function tacked on top of whichever function gets returned by the super call. Here is what I have so far:

from functools import partial
from sklearn.feature_extraction.text import TfidfVectorizer


class MyTfidfVectorizer(TfidfVectorizer):
    def __init__(self, some_file):
        super().__init__()
        f = open(some_file)
        self.suffixes = f.readlines()

    def build_preprocessor(self):
        existing_preprocessor = super(MyTfidfVectorizer, self).build_preprocessor()

        def additional_preprocessor(doc, suffixes, threshold=2):
            # Call existing_preprocessor here?
            processed_doc=doc
            return processed_doc
        return partial(additional_preprocessor, suffixes=self.suffixes)

I'm not sure how to chain the call hierarchy so when the preprocessing happens first the parent class' preprocessor is called and then the derived class' preprocessor gets called?