code: def test_sag_dataset(SAG_, bin_train_data):
      # make sure SAG/SAGA accept a Dataset object as argument
      X_bin, y_bin = bin_train_data
      clf1 = SAG_(eta=1e-3, max_iter=20, verbose=0, random_state=0)
      clf2 = SAG_(eta=1e-3, max_iter=20, verbose=0, random_state=0)
      clf1.fit(get_dataset(X_bin, order="C"), y_bin)
      clf2.fit(X_bin, y_bin)
      np.testing.assert_almost_equal(clf1.coef_, clf2.coef_)
  
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 6 for calling a global function with example usage: test_sag_dataset(SAG_, bin_train_data) without return types