class_docstr: Ridge extends LinearRegression by providing L2 regularization on the
  coefficients when predicting response y with a linear combination of the
  predictors in X. It can reduce the variance of the predictors, and improves
  the conditioning of the problem.
  
  cuML's dask Ridge (multi-node multi-gpu) expects dask cuDF
  DataFrame and provides an algorithms, Eig, to fit a linear model.
  And provides an eigendecomposition-based algorithm to fit a linear model.
  (SVD, which is more stable than eig, will be added in an upcoming version)
  Eig algorithm is usually preferred when the X is a tall and skinny matrix.
  As the number of features in X increases, the accuracy of Eig algorithm
  drops.
  
  This is an experimental implementation of dask Ridge Regression. It
  supports input X that has more than one column. Single column input
  X will be supported after SVD algorithm is added in an upcoming version.
  
  Parameters
  ----------
  alpha : float (default = 1.0)
      Regularization strength - must be a positive float. Larger values
      specify stronger regularization. Array input will be supported later.
  solver : {'eig'}
      Eig uses a eigendecomposition of the covariance matrix, and is much
      faster.
      Other solvers will be supported in the future.
  fit_intercept : boolean (default = True)
      If True, Ridge adds an additional term c to correct for the global
      mean of y, modeling the response as "x * beta + c".
      If False, the model expects that you have centered the data.
  normalize : boolean (default = False)
      If True, the predictors in X will be normalized by dividing by it's L2
      norm.
      If False, no scaling will be done.
  
  Attributes
  ----------
  coef_ : array, shape (n_features)
      The estimated coefficients for the linear regression model.
  intercept_ : array
      The independent term. If `fit_intercept` is False, will be 0.
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code: def _create_model(sessionId, datatype, **kwargs):
      from cuml.linear_model.ridge_mg import RidgeMG
  
      handle = get_raft_comm_state(sessionId, get_worker())["handle"]
      return RidgeMG(handle=handle, output_type=datatype, **kwargs)
  
file_path: cuml/python/cuml/dask/linear_model/ridge.py
imports: from cuml.dask.common.base import BaseEstimator
  from cuml.dask.common.base import DelayedPredictionMixin
  from cuml.dask.common.base import mnmg_import
  from cuml.dask.common.base import SyncFitMixinLinearModel
  from raft_dask.common.comms import get_raft_comm_state
  from dask.distributed import get_worker
label_desc: Use image node_id 5 for calling the Ridge obj's underlying member method code with example usage: obj._create_model(sessionId, datatype) and returns: RidgeMG