class_docstr: Estimator for learning linear regressors by SVRG.
  
  Solves the following objective:
  
  .. code-block::
  
      minimize_w  1 / n_samples * \sum_i loss(w^T x_i, y_i)
                  + alpha * 0.5 * ||w||^2_2
code: def fit(self, X, y):
      self.outputs_2d_ = len(y.shape) > 1
      Y = y.reshape(-1, 1) if not self.outputs_2d_ else y
      Y = Y.astype(np.float64)
      return self._fit(X, Y)
  
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 3 for calling the SVRGRegressor obj's underlying member method code with example usage: obj.fit(X, y) and returns: self