code: def fit(self, X, y):
      self._set_label_transformers(y)
      y = np.asfortranarray(
          self.label_binarizer_.transform(y), dtype=np.float64
      )
  
      if self.eta is None or self.eta == "auto":
          eta = get_auto_step_size(
              get_dataset(X, order="c"),
              self.alpha,
              self.loss,
              self.is_saga,
          )
      else:
          eta = self.eta
  
      if self.alpha * eta == 1:
          # to match the beaviour of SAGA
          # in this case SAGA decreases slightly eta
          eta *= 0.9
  
      loss = self._get_loss(self.loss)
      self.penalty = self._get_penalty(self.penalty)
  
      if not self.is_saga and self.penalty is not None:
          raise ValueError(
              "PySAGClassifier only accepts l2 penalty. Please "
              "use `saga=True` or PySAGAClassifier."
          )
  
      if self.is_saga:
          self.coef_ = _fit_saga(
              X,
              y,
              eta,
              self.alpha,
              loss,
              self.penalty,
              self.max_iter,
              self.rng,
          )
      else:
          self.coef_ = _fit_sag(
              X, y, eta, self.alpha, loss, self.max_iter, self.rng
          )
  
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 4 for calling the PySAGClassifier obj's underlying member method code with example usage: obj.fit(X, y) without return types