def METHOD_NAME(theta, d):
    """
    Spatial independence correlation model (pure nugget).
    (Useful when one wants to solve an ordinary least squares problem!)::
                                           n
        theta, d --> r(theta, d) = 1 if   sum |d_i| == 0
                                         i = 1
                                   0 otherwise
    Parameters
    ----------
    theta : array_like
        None.
    d : array_like
        An array with shape (n_eval, n_features) giving the componentwise
        distances between locations x and x' at which the correlation model
        should be evaluated.
    Returns
    -------
    r : array_like
        An array with shape (n_eval, ) with the values of the autocorrelation
        model.
    """
    theta = np.asarray(theta, dtype=np.float64)
    d = np.asarray(d, dtype=np.float64)
    n_eval = d.shape[0]
    r = np.zeros(n_eval)
    r[np.all(d == 0., axis=1)] = 1.
    return r