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DS-1000 / 853 / Is it possible to pass a custom function as a preprocessor to TfidfVectorizer?

Problem

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Problem:

Is it possible to pass a custom function as a preprocessor to TfidfVectorizer?
I want to write a function "prePro" that can turn every capital letter to lowercase letter.
Then somehow set the processor parameter to TfidfTVectorizer like "preprocessor=prePro". However, it doesn't work. I searched a lot but didn't find any examples useful.
Can anyone help me about this?

A:

<code>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
</code>
solve this question with example variable `tfidf`
BEGIN SOLUTION
<code>

library

Sklearn

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Source and history

upstream_id / Original problem ID
853
task_id
b3873e64-1995-5156-965a-5d4a8b8b27e3
task_key
default--test--853
task_revision_id
1
config / split
default / test

Official source

initial import