How to calculate TF-IDF values of noun documents excluding spaCy stop words?

Question

I have a data frame, df with text, cleaned_text, and nouns as column names. text and cleaned_text contains string document, nouns is a list of nouns extracted from cleaned_text column. df.shape = (1927, 3).

I am trying to calculate TF-IDF values for all documents within df only for nouns, excluding spaCy stopwords.

---

What I have tried?

import spacy
from spacy.lang.en import English

nlp = spacy.load('en_core_web_sm')

# subclass to modify stop word lists recommended from spaCy version 3.0 onwards
excluded_stop_words = {'down'}
included_stop_words = {'dear', 'regards'}

class CustomEnglishDefaults(English.Defaults):
    stop_words = English.Defaults.stop_words.copy()
    stop_words -= excluded_stop_words
    stop_words |= included_stop_words
    
class CustomEnglish(English):
    Defaults = CustomEnglishDefaults

# function to extract nouns from cleaned_text column, excluding spaCy stowords.
nlp = CustomEnglish()

def nouns(text):
    doc = nlp(text)
    return [t for t in doc if t.pos_ in ['NOUN'] and not t.is_stop and not t.is_punct]

# calculate TF-IDF values for nouns, excluding spaCy stopwords.
from sklearn.feature_extraction.text import TfidfVectorizer

documents = df.cleaned_text

tfidf = TfidfVectorizer(stop_words=CustomEnglish)
X = tfidf.fit_transform(documents)

---

What I am expecting?

I am expecting to have an output as a list of tuples ranked in descending order; nouns = [('noun_1', tf-idf_1), ('noun_2', tf-idf_2), ...]. All nouns in nouns should match those of df.nouns (this is to check whether I am on the right way).

---

What is my issue?

I got confused about how to apply TfidfVectorizer such that to calculate only TF-IDF values for Nouns extracted from cleaned_text. I am also not sure whether SkLearn TfidfVectorizer can calculate TF-IDF as I am expecting.