code: def __init__(
      self,
      eta,
      alpha=1.0,
      beta=0.0,
      loss="smooth_hinge",
      penalty="l2",
      gamma=1.0,
      max_iter=100,
      random_state=None,
      callback=None,
  ):
      super().__init__(
          eta=eta,
          alpha=alpha,
          beta=beta,
          loss=loss,
          penalty=penalty,
          gamma=gamma,
          max_iter=max_iter,
          random_state=random_state,
          callback=callback,
      )
      self.is_saga = True
  
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 1 to create a new PySAGAClassifier object from inherited base classes: PySAGClassifier with example: obj = PySAGAClassifier(eta, alpha, beta, loss, penalty, gamma, max_iter, random_state, callback)