code: def test_sag_proba():
      n_samples = 10
      X, y = make_classification(n_samples=n_samples, random_state=0)
      sag = SAGClassifier(
          eta=1e-3,
          alpha=0.0,
          beta=0.0,
          max_iter=10,
          loss="log",
          random_state=0,
      )
      sag.fit(X, y)
      check_predict_proba(sag, X)
  
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 8 for calling a global function with example usage: test_sag_proba() without return types