code: def _fit_sag(X, y, eta, alpha, loss, max_iter, rng):
      if sparse.issparse(X):
          X = X.toarray()
  
      n_samples, n_features = X.shape
      n_vectors = y.shape[1]
      g = np.empty((n_samples, n_features))
      coef_ = np.zeros((n_vectors, n_features))
  
      # Initialize gradients
      for i in range(n_samples):
          p = coef_.dot(X[i])
          g[i] = -loss.get_update(p, y[i]) * X[i]
      d = np.sum(g, axis=0)
  
      # Main loop
      for _ in range(max_iter):
          for _ in range(n_samples):
              i = rng.randint(n_samples - 1)
              p = coef_.dot(X[i])
              gi = -loss.get_update(p, y[i]) * X[i]
              coef_ -= eta * (
                  (gi - g[i] + d) / n_samples + alpha * coef_
              )
              d += gi - g[i]
              g[i] = gi
      return coef_
  
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 2 for calling a global function with example usage: _fit_sag(X, y, eta, alpha, loss, max_iter, rng) and returns: coef_