k usage), ``coef_`` is then a 1D array.
    Note that ``coef_`` stores the transpose of ``W``, ``W.T``.

n_iter_ : int
    Number of iterations run by the coordinate descent solver to reach
    the specified tolerance.

dual_gap_ : float
    The dual gaps at the end of the optimization.

eps_ : float
    The tolerance scaled scaled by the variance of the target `y`.

sparse_coef_ : sparse matrix of shape (n_features,) or             (n_targets, n_features)
    Sparse representation of the `coef_`.

n_features_in_ : int
    Number of features seen during :term:`fit`.

    .. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
    Names of features seen during :term:`fit`. Defined only when `X`
    has feature names that are all strings.

    .. versionadded:: 1.0

See Also
--------
MultiTaskElasticNetCV : Multi-task L1/L2 ElasticNet with built-in
    cross-validation.
ElasticNet : Linear regression with combined L1 and L2 priors as regularizer.
MultiTaskLasso : Multi-task Lasso model trained with L1/L2
    mixed-norm as regularizer.

Notes
-----
The algorithm used to fit the model is coordinate descent.

To avoid unnecessary memory duplication the X and y arguments of the fit
method should be directly passed as Fortran-contiguous numpy arrays.

Examples
--------
>>> from sklearn import linear_model
>>> clf = linear_model.MultiTaskElasticNet(alpha=0.1)
>>> clf.fit([[0,0], [1, 1], [2, 2]], [[0, 0], [1, 1], [2, 2]])
MultiTaskElasticNet(alpha=0.1)
>>> print(clf.coef_)
[[0.45663524 0.45612256]
 [0.45663524 0.45612256]]
>>> print(clf.intercept_)
[0.0872422 0.0872422]
rÄ