Whether to return the number of iterations or not.

positive : bool, default=False
    If set to True, forces coefficients to be positive.
    (Only allowed when ``y.ndim == 1``).

check_input : bool, default=True
    If set to False, the input validation checks are skipped (including the
    Gram matrix when provided). It is assumed that they are handled
    by the caller.

**params : kwargs
    Keyword arguments passed to the coordinate descent solver.

Returns
-------
alphas : ndarray of shape (n_alphas,)
    The alphas along the path where models are computed.

coefs : ndarray of shape (n_features, n_alphas) or             (n_targets, n_features, n_alphas)
    Coefficients along the path.

dual_gaps : ndarray of shape (n_alphas,)
    The dual gaps at the end of the optimization for each alpha.

n_iters : list of int
    The number of iterations taken by the coordinate descent optimizer to
    reach the specified tolerance for each alpha.
    (Is returned when ``return_n_iter`` is set to True).

See Also
--------
MultiTaskElasticNet : Multi-task ElasticNet model trained with L1/L2 mixed-norm     as regularizer.
MultiTaskElasticNetCV : Multi-task L1/L2 ElasticNet with built-in cross-validation.
ElasticNet : Linear regression with combined L1 and L2 priors as regularizer.
ElasticNetCV : Elastic Net model with iterative fitting along a regularization path.

Notes
-----
For an example, see
:ref:`examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.py
<sphx_glr_auto_examples_linear_model_plot_lasso_lasso_lars_elasticnet_path.py>`.

Examples
--------
>>> from sklearn.linear_model import enet_path
>>> from sklearn.datasets import make_regression
>>> X, y, true_coef = make_regression(
...    n_samples=100, n_features=5, n_informative=2, coef=True, random_state=0
... )
>>> true_coef
array([ 0.        ,  0.        ,  0.        , 97.9..., 45.7...])
>>> alphas, estimated_coef, _ = enet_path(X, y, n_alphas=3)
>>> alphas.shape
(3,)
>>> estimated_coef
 array([[ 0.        ,  0.78...,  0.56...],
        [ 0.        ,  1.12...,  0.61...],
        [-0.        , -2.12..., -1.12...],
        [ 0.        , 23.04..., 88.93...],
        [ 0.        , 10.63..., 41.56...]])
rb