code: def _fit(self, X, Y):
      n_samples, n_features = X.shape
      rng = self._get_random_state()
      loss = self._get_loss()
      n_vectors = Y.shape[1]
      n_inner = int(self.n_inner * n_samples)
      ds = get_dataset(X, order="c")
  
      self.coef_ = np.zeros((n_vectors, n_features), dtype=np.float64)
      full_grad = np.zeros_like(self.coef_)
      grad = np.zeros((n_vectors, n_samples), dtype=np.float64)
      self.coef_scale_ = np.ones(n_vectors, dtype=np.float64)
  
      for i in range(n_vectors):
          y = Y[:, i]
  
          _svrg_fit(
              self,
              ds,
              y,
              self.coef_[i],
              self.coef_scale_[i:],
              full_grad[i],
              grad[i],
              self.eta,
              self.alpha,
              loss,
              self.max_iter,
              n_inner,
              self.tol,
              self.verbose,
              self.callback,
              rng,
          )
  
      return self
  
imports: import numpy
  from .base import BaseClassifier, BaseRegressor
  from .dataset_fast import get_dataset
  from .svrg_fast import _svrg_fit
  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
label_desc: Use image node_id 2 for calling the _BaseSVRG obj's underlying member method code with example usage: obj._fit(X, Y) and returns: self