Can Autosklearn handle Multi-Class/Multi-Label Classification and which classifiers will it use?

I have been trying to use AutoSklearn with Multi-class classification

so my labels are like this

0 1 2 3 4 ... 200 1 0 1 1 1 ... 1 0 1 0 0 1 ... 0 1 0 0 1 0 ... 0 1 1 0 1 0 ... 1 0 1 1 0 1 ... 0 1 1 1 0 0 ... 1 1 0 1 0 1 ... 0

I used this code

y = y[:, (65,67,54,133,122,63,102,105,39)]
X = df.drop(Code, axis=1, errors='ignore')
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


automl = autosklearn.classification.AutoSklearnClassifier(
include={'feature_preprocessor': ["no_preprocessing"], 
 },
exclude={ 'classifier': ['random_forest']},
time_left_for_this_task=60*5,
per_run_time_limit=60*1,
memory_limit = 1024 * 10,
n_jobs=-1,
metric=autosklearn.metrics.f1_macro,
        )

but now I want to train Autosklearn on Multi-class Multi-label classification

Which method of these shall i use?

1-

clf = OneVsRestClassifier(automl, n_jobs=-1)
clf.fit(X_train, y_train)

2-

clf = automl
clf.fit(X_train, y_train)

3-

I have to loop one class at a time and use

clf = automl
clf.fit(X_train, y_train)

so it will be like

for i in (65,67,54,133,122,63,102,105,39):
       y = z[:, i]
       X = df.drop(Code, axis=1, errors='ignore')
       X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      automl = autosklearn.classification.AutoSklearnClassifier(
      include={'feature_preprocessor': ["no_preprocessing"], 
       },
      exclude={ 'classifier': ['random_forest']},
      time_left_for_this_task=60*5,
      per_run_time_limit=60*1,
      memory_limit = 1024 * 10,
      n_jobs=1,
      metric=autosklearn.metrics.f1_macro,
              )


      clf = automl
      clf.fit(X_train, y_train)

so I get a different model for each label?