def arnoldi_eigs(A: LinearOperator, start_vector: Array = None, max_iters: int = 100, tol: float = 1e-7,
                 use_householder: bool = False, pbar: bool = False, key=None):
    """
    Computes eigenvalues and eigenvectors using Arnoldi.

    Args:
        A (LinearOperator): A linear operator of size (n, n).
        start_vector (Array, optional): An initial vector to start Arnoldi of size (n, ).
         Defaults to a random probe.
        max_iters (int): The maximum number of iterations to run.
        tol (float, optional): Stopping criteria.
        use_householder (bool, optional): Use Householder Arnoldi variant.
        pbar (bool, optional): Show a progress bar.
        key (PNRGKey, optional): PRNGKey for random number generation.

    Returns:
        tuple:
            - eigvals (Array): eigenvalues of shape (max_iters,).
            - eigvectors (LinearOperator): eigenvectors of shape (n, max_iters).
            - info (dict): General information about the iterative procedure.
    """
    Q, H, info = arnoldi(A=A, start_vector=start_vector, max_iters=max_iters, tol=tol, use_householder=use_householder,
                         pbar=pbar, key=key)
    Q, H = Q[:, :-1], H[:-1]
    xnp = A.xnp
    eigvals, vs = xnp.eig(H.to_dense())
    eigvectors = Q @ lazify(vs)
    return eigvals, eigvectors, info

    Computes eigenvalues and eigenvectors using Arnoldi.

    Args:
        A (LinearOperator): A linear operator of size (n, n).
        start_vector (Array, optional): An initial vector to start Arnoldi of size (n, ).
         Defaults to a random probe.
        max_iters (int): The maximum number of iterations to run.
        tol (float, optional): Stopping criteria.
        use_householder (bool, optional): Use Householder Arnoldi variant.
        pbar (bool, optional): Show a progress bar.
        key (PNRGKey, optional): PRNGKey for random number generation.

    Returns:
        tuple:
            - eigvals (Array): eigenvalues of shape (max_iters,).
            - eigvectors (LinearOperator): eigenvectors of shape (n, max_iters).
            - info (dict): General information about the iterative procedure.
    
cola/linalg/decompositions/arnoldi.py