code: def test_elastic_saga(bin_train_data):
      X_bin, y_bin = bin_train_data
      ab = [1e-5, 1e-2, 1e-1, 1.0]
      for alpha, beta in zip(ab, ab):
          pysaga = PySAGAClassifier(
              eta=1e-3,
              alpha=alpha,
              beta=beta,
              max_iter=1,
              penalty="l1",
              random_state=0,
          )
          saga = SAGAClassifier(
              eta=1e-3,
              alpha=alpha,
              beta=beta,
              max_iter=1,
              penalty="l1",
              random_state=0,
              tol=0,
          )
          pysaga.fit(X_bin, y_bin)
          saga.fit(X_bin, y_bin)
          np.testing.assert_array_almost_equal(pysaga.coef_, saga.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 15 for calling a global function with example usage: test_elastic_saga(bin_train_data) without return types