How to use the pingouin.utils._is_sklearn_installed function in pingouin

Code inspired by Matlab code from Cyril Pernet and Guillaume
    Rousselet [1]_.

    Requires scikit-learn.

    References
    ----------

    .. [1] Pernet CR, Wilcox R, Rousselet GA. Robust Correlation Analyses:
       False Positive and Power Validation Using a New Open Source Matlab
       Toolbox. Frontiers in Psychology. 2012;3:606.
       doi:10.3389/fpsyg.2012.00606.
    """
    # Check that sklearn is installed
    from pingouin.utils import _is_sklearn_installed
    _is_sklearn_installed(raise_error=True)
    from scipy.stats import chi2
    from sklearn.covariance import MinCovDet
    X = np.column_stack((x, y))
    nrows, ncols = X.shape
    gval = np.sqrt(chi2.ppf(0.975, 2))

    # Compute center and distance to center
    center = MinCovDet(random_state=42).fit(X).location_
    B = X - center
    B2 = B**2
    bot = B2.sum(axis=1)

    # Loop over rows
    dis = np.zeros(shape=(nrows, nrows))
    for i in np.arange(nrows):
        if bot[i] != 0:

4. Return only the coefficients

    >>> logistic_regression(X, y, coef_only=True)
    array([-0.36736745, -0.04374684, -0.47829392])

    5. Passing custom parameters to sklearn

    >>> lom = logistic_regression(X, y, solver='sag', max_iter=10000,
    ...                           random_state=42)
    >>> print(lom['coef'].values)
    [-0.36751796 -0.04367056 -0.47841908]
    """
    # Check that sklearn is installed
    from pingouin.utils import _is_sklearn_installed
    _is_sklearn_installed(raise_error=True)
    from sklearn.linear_model import LogisticRegression

    # Extract names if X is a Dataframe or Series
    if isinstance(X, pd.DataFrame):
        names = X.keys().tolist()
    elif isinstance(X, pd.Series):
        names = [X.name]
    else:
        names = []

    # Convert to numpy array
    X = np.asarray(X)
    y = np.asarray(y)
    assert y.ndim == 1, 'y must be one-dimensional.'
    assert 0 < alpha < 1, 'alpha must be between 0 and 1.'