class_docstr: Lasso extends LinearRegression by providing L1 regularization on the
  coefficients when predicting response y with a linear combination of the
  predictors in X. It can zero some of the coefficients for feature
  selection and improves the conditioning of the problem.
  
  cuML's Lasso an array-like object or cuDF DataFrame and
  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 class.
      For numerical reasons, using alpha = 0 with the Lasso class is not
      advised.
      Given this, you should use the LinearRegression class.
  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.
  
  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 Lasso
  <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html>`_.
code: def __init__(self, *, client=None, **kwargs):
      super().__init__(client=client, **kwargs)
  
      kwargs["shuffle"] = False
  
      if "selection" in kwargs:
          if kwargs["selection"] == "random":
              kwargs["shuffle"] = True
  
          del kwargs["selection"]
  
      self.solver = CD(client=client, **kwargs)
  
imports: from cuml.dask.solvers import CD
  from cuml.dask.common.base import BaseEstimator
label_desc: Use image node_id 1 to create a new Lasso object from inherited base classes: BaseEstimator with example: obj = Lasso()