class_docstr: ElasticNet extends LinearRegression with combined L1 and L2 regularizations
  on the coefficients when predicting response y with a linear combination of
  the predictors in X. It can reduce the variance of the predictors, force
  some coefficients to be small, and improves the conditioning of the
  problem.
  
  cuML's ElasticNet an array-like object or cuDF DataFrame, uses coordinate
  descent to fit a linear model.
  
  Parameters
  ----------
  alpha : float (default = 1.0)
      Constant that multiplies the L1 term.
      alpha = 0 is equivalent to an ordinary least square, solved by the
      LinearRegression object.
      For numerical reasons, using alpha = 0 with the Lasso object is not
      advised.
      Given this, you should use the LinearRegression object.
  l1_ratio: float (default = 0.5)
      The ElasticNet mixing parameter, with 0 <= l1_ratio <= 1.
      For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is
      an L1 penalty.
      For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
  fit_intercept : boolean (default = True)
      If True, Lasso tries to correct for the global mean of y.
      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.
  max_iter : int (default = 1000)
      The maximum number of iterations
  tol : float (default = 1e-3)
      The tolerance for the optimization: if the updates are smaller than
      tol, the optimization code checks the dual gap for optimality and
      continues until it is smaller than tol.
  selection : {'cyclic', 'random'} (default='cyclic')
      If set to 'random', a random coefficient is updated every iteration
      rather than looping over features sequentially by default.
      This (setting to 'random') often leads to significantly faster
      convergence especially when tol is higher than 1e-4.
  handle : cuml.Handle
      Specifies the cuml.handle that holds internal CUDA state for
      computations in this model. Most importantly, this specifies the CUDA
      stream that will be used for the model's computations, so users can
      run different models concurrently in different streams by creating
      handles in several streams.
      If it is None, a new one is created.
  output_type : {'input', 'array', 'dataframe', 'series', 'df_obj',         'numba', 'cupy', 'numpy', 'cudf', 'pandas'}, default=None
      Return results and set estimator attributes to the indicated output
      type. If None, the output type set at the module level
      (`cuml.global_settings.output_type`) will be used. See
      :ref:`output-data-type-configuration` for more info.
  
  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.
  
  
  For additional docs, see `scikitlearn's ElasticNet
  <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html>`_.
docstr: Fit the model with X and y.
  
  Parameters
  ----------
  X : Dask cuDF DataFrame or CuPy backed Dask Array
      Dense matrix (floats or doubles) of shape (n_samples, n_features).
  
  y : Dask cuDF DataFrame or CuPy backed Dask Array
      Dense matrix (floats or doubles) of shape (n_samples, n_features).
docstr_tok: ["Fit","the","model","with","X","and","y.","Parameters","--","--","--","--","--","X",":","Dask","cuDF","DataFrame","or","CuPy","backed","Dask","Array","Dense","matrix","(","floats","or","doubles",")","of","shape","(","n_samples",",","n_features",")",".","y",":","Dask","cuDF","DataFrame","or","CuPy","backed","Dask","Array","Dense","matrix","(","floats","or","doubles",")","of","shape","(","n_samples",",","n_features",")","."]
code: def fit(self, X, y):
      """
      Fit the model with X and y.
  
      Parameters
      ----------
      X : Dask cuDF DataFrame or CuPy backed Dask Array
          Dense matrix (floats or doubles) of shape (n_samples, n_features).
  
      y : Dask cuDF DataFrame or CuPy backed Dask Array
          Dense matrix (floats or doubles) of shape (n_samples, n_features).
  
      """
  
      self.solver.fit(X, y)
      return self
  
imports: from cuml.dask.solvers import CD
  from cuml.dask.common.base import BaseEstimator
label_desc: Use image node_id 2 for calling the ElasticNet obj's underlying member method code with example usage: obj.fit(X, y) and returns: self