How to use the debtcollector.removals.remove function in debtcollector

    @removals.remove(version='1.7.0', removal_version='2.0.0')
    def post(self, url, **kwargs):
        """Perform an authenticate POST request.

        This calls :py:meth:`.request()` with ``method`` set to ``POST`` and an
        authentication token if one is available.

        .. warning::

            *DEPRECATED*: This function is no longer used and is deprecated as
            of the 1.7.0 release and may be removed in the 2.0.0 release. It
            was designed to be used by the managers and the managers now
            receive an adapter so this function is no longer on the standard
            request path.
        """
        return self._cs_request(url, 'POST', **kwargs)

    @removals.remove(
        message='Configuration of notifier is available through oslo.cfg',
        version='1.9.0',
        removal_version='3.0.0'
    )
    def config(self, config_dict):
        pass

@removals.remove(message="Please see dit.pid.PID_BROJA.", version='1.0.1')
def pi_decomp(d, d_opt):
    u0 = dit.multivariate.coinformation(d_opt, [[2], [0]], [1])
    u1 = dit.multivariate.coinformation(d_opt, [[2], [1]], [0])
    mi0 = dit.shannon.mutual_information(d_opt, [0], [2])
    rdn = mi0 - u0
    mi = dit.shannon.mutual_information(d, [0, 1], [2])
    syn = mi - mi0 - u1

    return syn, u0, u1, rdn

@removals.remove(
    message='keystoneclient auth plugins are deprecated. Use keystoneauth.',
    version='2.1.0',
    removal_version='3.0.0'
)
def get_plugin_options(name):
    """Get the oslo_config options for a specific plugin.

    This will be the list of config options that is registered and loaded by
    the specified plugin.

    :returns: A list of oslo_config options.
    """
    return base.get_plugin_class(name).get_options()

@removals.remove(
    message='use get_rpc_transport or get_notification_transport'
)
def get_transport(conf, url=None, allowed_remote_exmods=None):
    """A factory method for Transport objects.

    This method will construct a Transport object from transport configuration
    gleaned from the user's configuration and, optionally, a transport URL.

    If a transport URL is supplied as a parameter, any transport configuration
    contained in it takes precedence. If no transport URL is supplied, but
    there is a transport URL supplied in the user's configuration then that
    URL will take the place of the URL parameter. In both cases, any
    configuration not supplied in the transport URL may be taken from
    individual configuration parameters in the user's configuration.

    An example transport URL might be::

@removals.remove(message="Please use np.core.arrayprint.printoptions",
                 version='1.2.3')
@contextlib.contextmanager
def printoptions(strip_zeros=True, **kwargs):
    if strip_zeros:
        kwargs['trim'] = 'k'
    origcall = arrayprint.FloatingFormat.__call__

    def __call__(self, x):
        return origcall.__call__(self, x)

    arrayprint.FloatingFormat.__call__ = __call__
    original = np.get_printoptions()
    np.set_printoptions(**kwargs)
    yield
    np.set_printoptions(**original)
    arrayprint.FloatingFormat.__call__ = origcall

    @removals.remove(
        message="The healthcheck middleware must now be configured as "
        "an application, not as a filter")
    @classmethod
    def factory(cls, global_conf, **local_conf):
        return super(Healthcheck, cls).factory(global_conf, **local_conf)

@removals.remove(message="Please see methods in dit.algorithms.distribution_optimizers.py.",
                 version='1.0.1')
def isolate_zeros(dist, k):
    """
    Determines if there are any elements of the optimization vector that must
    be zero.

    If :math:`p(marginal) = 0`, then every component of the joint that
    contributes to that marginal probability must be exactly zero for all
    feasible solutions.
    """
    assert dist.is_dense()
    assert dist.get_base() == 'linear'

    d = get_abstract_dist(dist)
    n_variables = d.n_variables
    n_elements = d.n_elements

    @removals.remove(
        message='Configuration of notifier is available through oslo.cfg',
        version='1.9.0',
        removal_version='3.0.0'
    )
    def config(self, config=None):
        self._smtp_connect()

@removals.remove(message="Please see methods in dit.algorithms.distribution_optimizers.py.",
                 version='1.0.1')
def initial_point_generic(dist, rvs, A=None, b=None, isolated=None, **kwargs):
    """
    Find an initial point in the interior of the feasible set.

    """
    from cvxopt import matrix
    from cvxopt.modeling import variable

    if isolated is None:
        variables = isolate_zeros_generic(dist, rvs)
    else:
        variables = isolated

    if A is None or b is None:
        A, b = marginal_constraints_generic(dist, rvs)