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

def torontonian_sample_graph(A, n_mean, samples=1, max_photons=30, parallel=False):
    r"""Returns samples from the Torontonian of a Gaussian state specified by the adjacency matrix :math:`A`
    and with total mean photon number :math:`n_{mean}`

    Args:
        A (array): a :math:`N\times N` ``np.float64`` (symmetric) adjacency matrix matrix
        n_mean (float): mean photon number of the Gaussian state
        samples (int): the number of samples to return.
        max_photons (int): specifies the maximum number of photons that can be counted.
        parallel (bool): if ``True``, uses ``dask`` for parallelization of samples

    Returns:
        np.array[int]: photon number samples from the Torontonian of the Gaussian state
    """
    Q = gen_Qmat_from_graph(A, n_mean)
    cov = Covmat(Q, hbar=2)
    return torontonian_sample_state(cov, samples, hbar=2, max_photons=max_photons, parallel=parallel)

def torontonian_sample_graph(A, n_mean, samples=1, max_photons=30, parallel=False):
    r"""Returns samples from the Torontonian of a Gaussian state specified by the adjacency matrix :math:`A`
    and with total mean photon number :math:`n_{mean}`

    Args:
        A (array): a :math:`N\times N` ``np.float64`` (symmetric) adjacency matrix matrix
        n_mean (float): mean photon number of the Gaussian state
        samples (int): the number of samples to return.
        max_photons (int): specifies the maximum number of photons that can be counted.
        parallel (bool): if ``True``, uses ``dask`` for parallelization of samples

    Returns:
        np.array[int]: photon number samples from the Torontonian of the Gaussian state
    """
    Q = gen_Qmat_from_graph(A, n_mean)
    cov = Covmat(Q, hbar=2)
    return torontonian_sample_state(cov, samples, hbar=2, max_photons=max_photons, parallel=parallel)

Args:
        A (array): a :math:`N\times N` ``np.float64`` (symmetric) adjacency matrix matrix
        n_mean (float): mean photon number of the Gaussian state
        samples (int): the number of samples to return.
        cutoff (int): the Fock basis truncation.
        max_photons (int): specifies the maximum number of photons that can be counted.
        approx (bool): if ``True``, the approximate hafnian algorithm is used.
            Note that this can only be used for real, non-negative matrices.
        approx_samples: the number of samples used to approximate the hafnian if ``approx=True``.
        parallel (bool): if ``True``, uses ``dask`` for parallelization of samples

    Returns:
        np.array[int]: photon number samples from the Gaussian state
    """
    Q = gen_Qmat_from_graph(A, n_mean)
    cov = Covmat(Q, hbar=2)
    return hafnian_sample_state(
        cov,
        samples,
        mean=None,
        hbar=2,
        cutoff=cutoff,
        max_photons=max_photons,
        approx=approx,
        approx_samples=approx_samples,
        parallel=parallel,
    )

Args:
        A (array): a :math:`N\times N` ``np.float64`` (symmetric) adjacency matrix matrix
        n_mean (float): mean photon number of the Gaussian state
        samples (int): the number of samples to return.
        cutoff (int): the Fock basis truncation.
        max_photons (int): specifies the maximum number of photons that can be counted.
        approx (bool): if ``True``, the approximate hafnian algorithm is used.
            Note that this can only be used for real, non-negative matrices.
        approx_samples: the number of samples used to approximate the hafnian if ``approx=True``.
        parallel (bool): if ``True``, uses ``dask`` for parallelization of samples

    Returns:
        np.array[int]: photon number samples from the Gaussian state
    """
    Q = gen_Qmat_from_graph(A, n_mean)
    cov = Covmat(Q, hbar=2)
    return hafnian_sample_state(
        cov,
        samples,
        mean=None,
        hbar=2,
        cutoff=cutoff,
        max_photons=max_photons,
        approx=approx,
        approx_samples=approx_samples,
        parallel=parallel,
    )