code: def test_sag(clf, bin_train_data):
      X_bin, y_bin = bin_train_data
      clf.fit(X_bin, y_bin)
      assert not hasattr(clf, "predict_proba")
      assert clf.score(X_bin, y_bin) == 1.0
      assert list(clf.classes_) == [-1, 1]
  
imports: import numpy
  import pytest
  from scipy import sparse
  from sklearn.datasets import make_classification
  from lightning.impl.base import BaseClassifier
  from lightning.impl.dataset_fast import get_dataset
  from lightning.classification import SAGClassifier, SAGAClassifier
  from lightning.regression import SAGRegressor, SAGARegressor
  from lightning.impl.sgd_fast import ModifiedHuber
  from lightning.impl.sgd_fast import SmoothHinge
  from lightning.impl.sgd_fast import SquaredHinge
  from lightning.impl.sgd_fast import Log
  from lightning.impl.sgd_fast import SquaredLoss
  from lightning.impl.sag import get_auto_step_size
  from lightning.impl.tests.utils import check_predict_proba
label_desc: Use image node_id 5 for calling a global function with example usage: test_sag(clf, bin_train_data) without return types