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