How to use a set of pre-defined classifiers in Adaboost?
One possible solution is using a Stacking classifier as follows:
from sklearn.ensemble import StackingClassifier
# Assumed you already import  all the models you are using

dt = DecisionTreeClassifier(max_depth=DT_max_depth, random_state=0)
rf = RandomForestClassifier(n_estimators=RF_n_est, random_state=0)
xgb = XGBClassifier(n_estimators=XGB_n_est, random_state=0)
knn = KNeighborsClassifier(n_neighbors=KNN_n_neigh)
svm1 = svm.SVC(kernel='linear')
svn2 = svm.SVC(kernel='rbf')
lr = LogisticRegression(random_state=0,penalty = LR_n_est, solver= 'saga')

estimators = [
    ('rf', rf),
    ('svm1', svm1), ('svn2', svn2), ('xgb', xgb), ('knn', knn), ('lr', lr), ('dt', dt)
]

Option 1:
stacker = AdaBoostClassifier()   
model = StackingClassifier(
    estimators=estimators, final_estimator= stacker
)

Option 2:
base_model = StackingClassifier(
    estimators=estimators
)
model = AdaBoostClassifier(base_estimator = base_model)

Option 2 will be without question the most expensive* of both and as far as I understand, option 2 fits better what you are looking for.

Strategy: