)
    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
--------
Lasso: Linear Model trained with L1 prior as regularizer (aka the Lasso).
MultiTaskLassoCV: Multi-task L1 regularized linear model with built-in
    cross-validation.
MultiTaskElasticNetCV: Multi-task L1/L2 ElasticNet with built-in cross-validation.

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.MultiTaskLasso(alpha=0.1)
>>> clf.fit([[0, 1], [1, 2], [2, 4]], [[0, 0], [1, 1], [2, 3]])
MultiTaskLasso(alpha=0.1)
>>> print(clf.coef_)
[[0.         0.60809415]
[0.         0.94592424]]
>>> print(clf.intercept_)
[-0.41888636 -0.87382323]
r