code: def test_sag_regression(SAG_, bin_train_data):
      X_bin, y_bin = bin_train_data
      reg = SAG_(random_state=0, loss="squared")
      reg.fit(X_bin, y_bin)
      y_pred = np.sign(reg.predict(X_bin))
      assert np.mean(y_bin == y_pred) == 1.0
  
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 19 for calling a global function with example usage: test_sag_regression(SAG_, bin_train_data) without return types