code: def __init__(
      self,
      alpha=1.0,
      fit_intercept=True,
      normalize=False,
      copy_X=True,
      max_iter=None,
      tol=1e-3,
      solver="auto",
      random_state=None,
  ):
      self.alpha = alpha
      self.fit_intercept = fit_intercept
      self.normalize = normalize
      self.copy_X = copy_X
      self.max_iter = max_iter
      self.tol = tol
      self.solver = solver
      self.random_state = random_state
  
imports: from abc import ABCMeta, abstractmethod
  import warnings
  import numpy
  from scipy import linalg
  from scipy import sparse
  from scipy.sparse import linalg
  from .base import LinearClassifierMixin, LinearModel, _rescale_data
  from .sag import sag_solver
  from ..base import RegressorMixin
  from ..utils.extmath import safe_sparse_dot
  from ..utils.extmath import row_norms
  from ..utils import check_X_y
  from ..utils import check_array
  from ..utils import check_consistent_length
  from ..utils import compute_sample_weight
  from ..utils import column_or_1d
  from ..preprocessing import LabelBinarizer
  from ..model_selection import GridSearchCV
  from ..externals import six
  from ..metrics.scorer import check_scoring
label_desc: Use image node_id 1 to create a new _BaseRidge object from inherited base classes:  with example: obj = _BaseRidge(alpha, fit_intercept, normalize, copy_X, max_iter, tol, solver, random_state)