How to use the celerite.solver function in celerite

V = U * np.random.rand(4)[:, None]
        A = np.sum(U * V, axis=0) + 1e-8
    else:
        A = np.empty(0)
        U = np.empty((0, 0))
        V = np.empty((0, 0))

    # Check quiet argument with a non-positive definite kernel.
    class NPDTerm(terms.Term):
        parameter_names = ("par1", )
        def get_real_coefficients(self, params):  # NOQA
            return [params[0]], [0.1]
    gp = GP(NPDTerm(-1.0))
    with pytest.raises(celerite.solver.LinAlgError):
        gp.compute(x, 0.0)
    with pytest.raises(celerite.solver.LinAlgError):
        gp.log_likelihood(y)
    assert np.isinf(gp.log_likelihood(y, quiet=True))
    if terms.HAS_AUTOGRAD:
        assert np.isinf(gp.grad_log_likelihood(y, quiet=True)[0])

    kernel = terms.RealTerm(0.1, 0.5)
    gp = GP(kernel)
    with pytest.raises(RuntimeError):
        gp.log_likelihood(y)

    termlist = [(0.1 + 10./j, 0.5 + 10./j) for j in range(1, 4)]
    termlist += [(1.0 + 10./j, 0.01 + 10./j, 0.5, 0.01) for j in range(1, 10)]
    termlist += [(0.6, 0.7, 1.0), (0.3, 0.05, 0.5, 0.6)]
    for term in termlist:
        if len(term) > 2:
            kernel += terms.ComplexTerm(*term)

if with_general:
        U = np.vander(x - np.mean(x), 4).T
        V = U * np.random.rand(4)[:, None]
        A = np.sum(U * V, axis=0) + 1e-8
    else:
        A = np.empty(0)
        U = np.empty((0, 0))
        V = np.empty((0, 0))

    # Check quiet argument with a non-positive definite kernel.
    class NPDTerm(terms.Term):
        parameter_names = ("par1", )
        def get_real_coefficients(self, params):  # NOQA
            return [params[0]], [0.1]
    gp = GP(NPDTerm(-1.0))
    with pytest.raises(celerite.solver.LinAlgError):
        gp.compute(x, 0.0)
    with pytest.raises(celerite.solver.LinAlgError):
        gp.log_likelihood(y)
    assert np.isinf(gp.log_likelihood(y, quiet=True))
    if terms.HAS_AUTOGRAD:
        assert np.isinf(gp.grad_log_likelihood(y, quiet=True)[0])

    kernel = terms.RealTerm(0.1, 0.5)
    gp = GP(kernel)
    with pytest.raises(RuntimeError):
        gp.log_likelihood(y)

    termlist = [(0.1 + 10./j, 0.5 + 10./j) for j in range(1, 4)]
    termlist += [(1.0 + 10./j, 0.01 + 10./j, 0.5, 0.01) for j in range(1, 10)]
    termlist += [(0.6, 0.7, 1.0), (0.3, 0.05, 0.5, 0.6)]
    for term in termlist:

def logprob(self):

        self.update_kernel_params()

        try:
            solver = self.kernel.compute_covmatrix(self.errorbars())

            # calculate log likelihood
            lnlike = -0.5 * (solver.dot_solve(self._resids()) + solver.log_determinant() + self.N*np.log(2.*np.pi))

            return lnlike

        except celerite.solver.LinAlgError:
            warnings.warn("Non-positive definite kernel detected.", RuntimeWarning)
            return -np.inf

try:
    __CELERITE_SETUP__
except NameError:
    __CELERITE_SETUP__ = False

if not __CELERITE_SETUP__:
    __all__ = [
        "terms", "solver", "modeling", "GP", "CholeskySolver",
        "__library_version__",
    ]

    from . import terms, solver, modeling
    from .celerite import GP
    from .solver import CholeskySolver
    __library_version__ = solver.get_library_version()

def solver(self):
        if self._solver is None:
            self._solver = solver.CholeskySolver()
        return self._solver

quiet (bool): If true, return ``-numpy.inf`` for non-positive
                definite matrices instead of throwing an error.

        Returns:
            float: The marginalized likelihood of the GP model.

        Raises:
            ValueError: For mismatched dimensions.
            solver.LinAlgError: For non-positive definite matrices.

        """
        y = self._process_input(y)
        resid = y - self.mean.get_value(self._t)
        try:
            self._recompute()
        except solver.LinAlgError:
            if quiet:
                return -np.inf
            raise
        if len(y.shape) > 1:
            raise ValueError("dimension mismatch")
        logdet = self.solver.log_determinant()
        if not np.isfinite(logdet):
            return -np.inf
        loglike = -0.5*(self.solver.dot_solve(resid)+logdet+len(y)*_const)
        if not np.isfinite(loglike):
            return -np.inf
        return loglike

y (array[n]): The observations at coordinates ``x`` from
                :func:`GP.compute`.
            quiet (bool): If true, return ``-numpy.inf`` and a gradient vector
                of zeros for non-positive definite matrices instead of
                throwing an error.

        Returns:
            The gradient of marginalized likelihood with respect to the
            parameter vector.

        Raises:
            ValueError: For mismatched dimensions.
            solver.LinAlgError: For non-positive definite matrices.

        """
        if not solver.has_autodiff():
            raise RuntimeError("celerite must be compiled with autodiff "
                               "support to use the gradient methods")

        if not self.kernel.vector_size:
            return self.log_likelihood(y, quiet=quiet), np.empty(0)

        y = self._process_input(y)
        if len(y.shape) > 1:
            raise ValueError("dimension mismatch")
        resid = y - self.mean.get_value(self._t)

        (alpha_real, beta_real, alpha_complex_real, alpha_complex_imag,
         beta_complex_real, beta_complex_imag) = self.kernel.coefficients
        try:
            val, grad = self.solver.grad_log_likelihood(
                self.kernel.jitter,