code: def _fit(self, X, Y, sample_weight):
      rng = self._get_random_state()
      adaptive_step_size = False
      ds = get_dataset(X, order="c")
      n_samples, n_features = ds.get_n_samples(), ds.get_n_features()
  
      if sample_weight is None:
          sample_weight = np.ones(n_samples, dtype=np.float64)
      else:
          sample_weight = np.asarray(sample_weight, dtype=np.float64)
  
      if self.eta is None or self.eta in ("auto", "line-search"):
          step_size = get_auto_step_size(
              ds,
              self.alpha,
              self.loss,
              self.gamma,
              sample_weight=sample_weight,
          )
          if self.verbose > 0:
              print("Auto stepsize: %s" % self.eta)
          if self.eta == "line-search":
              self.eta = step_size
              adaptive_step_size = True
          else:
              self.eta = step_size
  
      loss = self._get_loss()
      penalty = self._get_penalty()
      n_vectors = Y.shape[1]
      n_inner = int(self.n_inner * n_samples)
  
      self.coef_ = np.zeros((n_vectors, n_features), dtype=np.float64)
      self.coef_scale_ = np.ones(n_vectors, dtype=np.float64)
      grad = np.zeros((n_vectors, n_samples), dtype=np.float64)
  
      for i in range(n_vectors):
          y = Y[:, i]
  
          _sag_fit(
              self,
              ds,
              y,
              self.coef_[i],
              self.coef_scale_[i:],
              grad[i],
              sample_weight,
              self.eta,
              self.alpha,
              self.beta,
              loss,
              penalty,
              self.max_iter,
              n_inner,
              self.tol,
              self.verbose,
              self.callback,
              rng,
              self.is_saga,
              adaptive_step_size,
          )
  
      return self
  
imports: import numpy
  from .base import BaseClassifier, BaseRegressor
  from .dataset_fast import get_dataset
  from .sag_fast import _sag_fit, get_auto_step_size
  from .sgd_fast import ModifiedHuber
  from .sgd_fast import SmoothHinge
  from .sgd_fast import SquaredHinge
  from .sgd_fast import Log
  from .sgd_fast import SquaredLoss
  from .sag_fast import L1Penalty
label_desc: Use image node_id 4 for calling the _BaseSAG obj's underlying member method code with example usage: obj._fit(X, Y, sample_weight) and returns: self