How to use the emcee.backends.Backend function in emcee

def test_restart(backend, dtype):
    # Run a sampler with the default backend.
    b = backends.Backend()
    run_sampler(b, dtype=dtype)
    sampler1 = run_sampler(b, seed=None, dtype=dtype)

    with backend() as be:
        run_sampler(be, dtype=dtype)
        sampler2 = run_sampler(be, seed=None, dtype=dtype)

        # Check all of the components.
        for k in ["chain", "log_prob", "blobs"]:
            a = getattr(sampler1, "get_" + k)()
            b = getattr(sampler2, "get_" + k)()
            _custom_allclose(a, b)

        last1 = sampler1.get_last_sample()
        last2 = sampler2.get_last_sample()
        assert np.allclose(last1.coords, last2.coords)

@pytest.mark.parametrize("backend,dtype,blobs",
                         product(other_backends, dtypes, [True, False]))
def test_backend(backend, dtype, blobs):
    # Run a sampler with the default backend.
    sampler1 = run_sampler(backends.Backend(), dtype=dtype, blobs=blobs)

    with backend() as be:
        sampler2 = run_sampler(be, dtype=dtype, blobs=blobs)

        values = ["chain", "log_prob"]
        if blobs:
            values += ["blobs"]
        else:
            assert sampler1.get_blobs() is None
            assert sampler2.get_blobs() is None

        # Check all of the components.
        for k in values:
            a = getattr(sampler1, "get_" + k)()
            b = getattr(sampler2, "get_" + k)()
            _custom_allclose(a, b)

# continued_accepteds = continue_from_ps.get_value(qualifier='accepteds', **_skip_filter_checks)
                # # continued_accepted [iterations, walkers]
                continued_acceptance_fractions = continue_from_ps.get_value(qualifier='acceptance_fractions', **_skip_filter_checks)
                # continued_acceptance_fractions [iterations, walkers]
                continued_lnprobabilities = continue_from_ps.get_value(qualifier='lnprobabilities', **_skip_filter_checks)
                # continued_lnprobabilities [iterations, walkers]
                p0 = continued_samples[-1].T
                # p0 [parameter, walkers]
                nwalkers = int(p0.shape[-1])

                start_iteration = continued_lnprobabilities.shape[0]

                # fake a backend object from the previous solution so that emcee
                # can continue from where it left off and still compute
                # autocorrelation times, etc.
                backend = emcee.backends.Backend()
                backend.nwalkers = int(nwalkers)
                backend.ndim = int(len(params_uniqueids))
                backend.iteration = start_iteration
                backend.accepted = np.asarray(continued_acceptance_fractions * start_iteration, dtype='int')
                backend.chain = continued_samples
                backend.log_prob = continued_lnprobabilities
                backend.initialized = True
                backend.random_state = None
                esargs['backend'] = backend

            params_twigs = [b.get_parameter(uniqueid=uniqueid, **_skip_filter_checks).twig for uniqueid in params_uniqueids]

            esargs['pool'] = pool
            esargs['nwalkers'] = nwalkers
            esargs['ndim'] = len(params_uniqueids)
            esargs['log_prob_fn'] = _lnprobability

# continued_accepteds = continue_from_ps.get_value(qualifier='accepteds', **_skip_filter_checks)
                # # continued_accepted [iterations, walkers]
                continued_acceptance_fractions = continue_from_ps.get_value(qualifier='acceptance_fractions', **_skip_filter_checks)
                # continued_acceptance_fractions [iterations, walkers]
                continued_lnprobabilities = continue_from_ps.get_value(qualifier='lnprobabilities', **_skip_filter_checks)
                # continued_lnprobabilities [iterations, walkers]
                p0 = continued_samples[-1].T
                # p0 [parameter, walkers]
                nwalkers = int(p0.shape[-1])

                start_iteration = continued_lnprobabilities.shape[0]

                # fake a backend object from the previous solution so that emcee
                # can continue from where it left off and still compute
                # autocorrelation times, etc.
                backend = emcee.backends.Backend()
                backend.nwalkers = int(nwalkers)
                backend.ndim = int(len(params_uniqueids))
                backend.iteration = start_iteration
                backend.accepted = np.asarray(continued_acceptance_fractions * start_iteration, dtype='int')
                backend.chain = continued_samples
                backend.log_prob = continued_lnprobabilities
                backend.initialized = True
                backend.random_state = None
                # reconstructing blobs will be messy, so we'll just get the correct
                # shape with nones, but add to the existing dictionary later
                backend.blobs = np.full(tuple(list(continued_samples.shape[:-1])+[2]), fill_value=None)

                esargs['backend'] = backend

            params_twigs = [b.get_parameter(uniqueid=uniqueid, **_skip_filter_checks).twig for uniqueid in params_uniqueids]