code: def test_enet_regularized_saga(bin_train_data):
      X_sparse = sparse.rand(
          100, 50, density=0.5, random_state=0
      ).tocsr()
      y_sparse = np.random.randint(0, high=2, size=100)
  
      eta = 1e-3
      for X, y in (bin_train_data, (X_sparse, y_sparse)):
          for alpha in np.logspace(-3, 0, 5):
              for beta in np.logspace(-3, 3, 5):
                  pysaga = PySAGAClassifier(
                      eta=eta,
                      alpha=alpha,
                      beta=beta,
                      max_iter=5,
                      penalty="l1",
                      random_state=0,
                  )
                  saga = SAGAClassifier(
                      eta=eta,
                      alpha=alpha,
                      beta=beta,
                      max_iter=5,
                      penalty="l1",
                      random_state=0,
                      tol=1e-24,
                  )
  
                  pysaga.fit(X, y)
                  saga.fit(X, y)
                  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 13 for calling a global function with example usage: test_enet_regularized_saga(bin_train_data) without return types