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 __init__(
      self,
      eta=1.0,
      alpha=1.0,
      loss="squared",
      gamma=1.0,
      max_iter=10,
      n_inner=1.0,
      tol=1e-3,
      verbose=0,
      callback=None,
      random_state=None,
  ):
      self.eta = eta
      self.alpha = alpha
      self.loss = loss
      self.gamma = gamma
      self.max_iter = max_iter
      self.n_inner = n_inner
      self.tol = tol
      self.verbose = verbose
      self.callback = callback
      self.random_state = random_state
  
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 1 to create a new SVRGRegressor object from inherited base classes: BaseRegressor, _BaseSVRG with example: obj = SVRGRegressor(eta, alpha, loss, gamma, max_iter, n_inner, tol, verbose, callback, random_state)