def __call__(self, Xi, Xj, ni, nj, hyper_deriv=None, symmetric=False):
        """Return zeros the same length as the input Xi.
        
        Ignores all other arguments.
        
        Parameters
        ----------
        Xi : :py:class:`Matrix` or other Array-like, (`M`, `D`)
            `M` inputs with dimension `D`.
        Xj : :py:class:`Matrix` or other Array-like, (`M`, `D`)
            `M` inputs with dimension `D`.
        ni : :py:class:`Matrix` or other Array-like, (`M`, `D`)
            `M` derivative orders for set `i`.
        nj : :py:class:`Matrix` or other Array-like, (`M`, `D`)
            `M` derivative orders for set `j`.
        hyper_deriv : Non-negative int or None, optional
            The index of the hyperparameter to compute the first derivative
            with respect to. Since this kernel only has one hyperparameter, 0
            is the only valid value. If None, no derivatives are taken. Default
            is None (no hyperparameter derivatives).
        symmetric : bool, optional
            Whether or not the input `Xi`, `Xj` are from a symmetric matrix.
            Default is False.
        
        Returns
        -------
        Kij : :py:class:`Array`, (`M`,)
            Covariances for each of the `M` `Xi`, `Xj` pairs.
        """
        return scipy.zeros(Xi.shape[0], dtype=float)
def __call__ ( self , Xi , Xj , ni , nj , hyper_deriv = None , symmetric = False ) : return scipy . zeros ( Xi . shape [ 0 ] , dtype = float )
Return zeros the same length as the input Xi.
        
        Ignores all other arguments.
        
        Parameters
        ----------
        Xi : :py:class:`Matrix` or other Array-like, (`M`, `D`)
            `M` inputs with dimension `D`.
        Xj : :py:class:`Matrix` or other Array-like, (`M`, `D`)
            `M` inputs with dimension `D`.
        ni : :py:class:`Matrix` or other Array-like, (`M`, `D`)
            `M` derivative orders for set `i`.
        nj : :py:class:`Matrix` or other Array-like, (`M`, `D`)
            `M` derivative orders for set `j`.
        hyper_deriv : Non-negative int or None, optional
            The index of the hyperparameter to compute the first derivative
            with respect to. Since this kernel only has one hyperparameter, 0
            is the only valid value. If None, no derivatives are taken. Default
            is None (no hyperparameter derivatives).
        symmetric : bool, optional
            Whether or not the input `Xi`, `Xj` are from a symmetric matrix.
            Default is False.
        
        Returns
        -------
        Kij : :py:class:`Array`, (`M`,)
            Covariances for each of the `M` `Xi`, `Xj` pairs.