How to use the thewalrus.quantum.is_classical_cov function in thewalrus

):  # add cutoff for consistency pylint: disable=unused-argument
    r"""Returns samples from a Gaussian state that has a positive :math:`P` function.
    Args:
        cov(array): a :math:`2N\times 2N` ``np.float64`` covariance matrix
            representing an :math:`N` mode quantum state. This can be obtained
            via the ``scovmavxp`` method of the Gaussian backend of Strawberry Fields.
        samples (int): number of samples to generate
        mean (array): vector of means of the gaussian state
        hbar (float): the value of :math:`\hbar` in the commutation
            relation :math:`[\x,\p]=i\hbar`.
        sigdigits (integer): precision to check that the covariance matrix is a true covariance matrix of a gaussian state.

    Returns:
        np.array[int]: photon number samples from the Gaussian state with covariance cov and vector means mean.
    """
    if not is_classical_cov(cov, hbar=hbar, atol=atol):
        raise ValueError("Not a classical covariance matrix")

    (n, _) = cov.shape
    if mean is None:
        mean = np.zeros([n])
    else:
        if mean.shape != (n,):
            raise ValueError("mean and cov do not have compatible shapes")

    R = np.random.multivariate_normal(mean, cov - 0.5 * hbar * np.identity(n), samples)
    N = n // 2
    alpha = (1.0 / np.sqrt(2 * hbar)) * (R[:, 0:N] + 1j * R[:, N : 2 * N])
    samples = np.random.poisson(np.abs(alpha) ** 2)
    return samples

r"""Returns samples from a Gaussian state that has a positive :math:`P` function.

    Args:
        cov(array): a :math:`2N\times 2N` ``np.float64`` covariance matrix
            representing an :math:`N` mode quantum state. This can be obtained
            via the ``scovmavxp`` method of the Gaussian backend of Strawberry Fields.
        samples (int): number of samples to generate
        mean (array): vector of means of the gaussian state
        hbar (float): the value of :math:`\hbar` in the commutation
            relation :math:`[\x,\p]=i\hbar`.
        sigdigits (integer): precision to check that the covariance matrix is a true covariance matrix of a gaussian state.

    Returns:
        np.array[int]: photon number samples from the Gaussian state with covariance cov and vector means mean.
    """
    if not is_classical_cov(cov, hbar=hbar, atol=atol):
        raise ValueError("Not a classical covariance matrix")

    (n, _) = cov.shape
    if mean is None:
        mean = np.zeros([n])
    else:
        if mean.shape != (n,):
            raise ValueError("mean and cov do not have compatible shapes")

    R = np.random.multivariate_normal(mean, cov - 0.5 * hbar * np.identity(n), samples)
    N = n // 2
    alpha = (1.0 / np.sqrt(2 * hbar)) * (R[:, 0:N] + 1j * R[:, N : 2 * N])
    samples = np.random.poisson(np.abs(alpha) ** 2)
    return samples