def METHOD_NAME(data, grid, **kwargs):
    """
    Kernel Density Estimation with Scikit-learn
    Parameters
    ----------
    data : numpy.array
        Data points used to compute a density estimator. It
        has `n x p` dimensions, representing n points and p
        variables.
    grid : numpy.array
        Data points at which the desity will be estimated. It
        has `m x p` dimensions, representing m points and p
        variables.
    Returns
    -------
    out : numpy.array
        Density estimate. Has `m x 1` dimensions
    """
    # Not core dependency
    try:
        from sklearn.neighbors import KernelDensity
    except ImportError as err:
        raise ImportError("scikit-learn is not installed") from err
    kde_skl = KernelDensity(**kwargs)
    kde_skl.fit(data)
    # score_samples() returns the log-likelihood of the samples
    log_pdf = kde_skl.score_samples(grid)
    return np.exp(log_pdf)