code: def test_saga_score():
      X, y = make_classification(n_samples=1000, random_state=0)
  
      pysaga = PySAGAClassifier(
          eta=1e-3,
          alpha=0.0,
          beta=0.0,
          max_iter=1,
          penalty=None,
          random_state=0,
      )
      saga = SAGAClassifier(
          eta=1e-3,
          alpha=0.0,
          beta=0.0,
          max_iter=1,
          penalty=None,
          random_state=0,
      )
  
      pysaga.fit(X, y)
      saga.fit(X, y)
      assert pysaga.score(X, y) == saga.score(X, y)
  
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 12 for calling a global function with example usage: test_saga_score() without return types