How can I use Ensemble learning of two models with different features as an input?

I have a fake news detection problem and it predicts the binary labels "1"&"0" by vectorizing the 'tweet' column, I use three different models for detection but I want to use the ensemble method to increase the accuracy but they use different vectorezer.

I have 3 KNN models the first and the second one vectorizes the 'tweet' column using TF-IDF.

from sklearn.feature_extraction.text import TfidfVectorizer
vector = TfidfVectorizer(max_features =5000, ngram_range=(1,3))
X_train = vector.fit_transform(X_train['tweet']).toarray()
X_test = vector.fit_transform(X_test['tweet']).toarray()


for the third model I used fastText for sentence vectorization

%%time
sent_vec = []
for index, row in X_train.iterrows():
sent_vec.append(avg_feature_vector(row['tweet']))
%%time
sent_vec1 = []
for index, row in X_test.iterrows():
sent_vec1.append(avg_feature_vector(row['tweet']))


after scaling and... my third model fits the input like this

scaler.fit(sent_vec)
scaled_X_train= scaler.transform(sent_vec)
scaled_X_test= scaler.transform(sent_vec1)
.
.
.
knn_model1.fit(scaled_X_train, y_train)


now I want to combine the three models like this and I want the ensemble method to give me the majority just likeVotingClassifier, but I have no idea how can I deal with the different inputs (TF-IDF & fastText) is there another way to do that?