How to use the elfi.new_model function in elfi

vsim = elfi.tools.vectorize(lsimulator)
    vsum = elfi.tools.vectorize(lsummary)

    v = vsim(np.array([[.2, .8], [.3, .7]]))
    assert is_array(v)
    assert not isinstance(v[0], list)

    vsim = elfi.tools.vectorize(lsimulator, dtype=False)

    v = vsim(np.array([[.2, .8], [.3, .7]]))
    assert is_array(v)
    assert isinstance(v[0], list)

    obs = lsimulator([.2, .8])

    elfi.new_model()
    p = elfi.Prior('dirichlet', [2, 2])
    sim = elfi.Simulator(vsim, p, observed=obs)
    S = elfi.Summary(vsum, sim)
    d = elfi.Distance('euclidean', S)

    pool = elfi.OutputPool(['sim'])
    rej = elfi.Rejection(d, batch_size=100, pool=pool, output_names=['sim'])
    sample = rej.sample(100, n_sim=1000)
    mean = np.mean(sample.samples['p'], axis=0)

    # Crude test
    assert mean[1] > mean[0]

def test_external_operation():
    # Note that the test string has intentionally not uniform formatting with spaces
    elfi.new_model()
    op = elfi.tools.external_operation('echo 1 {0} 4  5    6 {seed}')
    constant = elfi.Constant(123)
    simulator = elfi.Simulator(op, constant)
    v = simulator.generate(1)
    assert np.array_equal(v[:5], [1, 123, 4, 5, 6])

    # Can be pickled
    pickle.dumps(op)

def test_dict_output():
    vsim = elfi.tools.vectorize(simulator)
    vsum = elfi.tools.vectorize(summary)

    obs = simulator([.2, .8])

    elfi.new_model()
    p = elfi.Prior('dirichlet', [2, 2])
    sim = elfi.Simulator(vsim, p, observed=obs)
    S = elfi.Summary(vsum, sim)
    d = elfi.Distance('euclidean', S)

    pool = elfi.OutputPool(['sim'])
    rej = elfi.Rejection(d, batch_size=100, pool=pool, output_names=['sim'])
    sample = rej.sample(100, n_sim=1000)
    mean = np.mean(sample.samples['p'], axis=0)

    # Crude test
    assert mean[1] > mean[0]

if true_params is None:
        if nd_mean:
            true_params = [4, 4]  # 2-D mean.
        else:
            true_params = [4, .4]  # mean and standard deviation.

    # Choosing the simulator for both observations and simulations.
    if nd_mean:
        fn_simulator = partial(gauss_nd_mean, cov_matrix=cov_matrix, n_obs=n_obs)
    else:
        fn_simulator = partial(gauss, n_obs=n_obs)

    # Obtaining the observations.
    y_obs = fn_simulator(*true_params, n_obs=n_obs, random_state=np.random.RandomState(seed_obs))

    m = elfi.new_model()
    # Initialising the priors.
    eps_prior = 5  # The longest distance from the median of an initialised prior's distribution.
    priors = []
    if nd_mean:
        n_dim = len(true_params)
        for i in range(n_dim):
            name_prior = 'mu_{}'.format(i)
            prior_mu = elfi.Prior('uniform', true_params[i] - eps_prior,
                                  2 * eps_prior, model=m, name=name_prior)
            priors.append(prior_mu)
    else:
        priors.append(elfi.Prior('uniform', true_params[0] - eps_prior,
                                 2 * eps_prior, model=m, name='mu'))
        priors.append(elfi.Prior('truncnorm', np.amax([.01, true_params[1] - eps_prior]),
                                 2 * eps_prior, model=m, name='sigma'))
    elfi.Simulator(fn_simulator, *priors, observed=y_obs, name='gauss')

"""Return an initialised bivariate g-and-k model.

    Parameters
    ----------
    n_obs : int, optional
        Number of the observations.
    true_params : array_like, optional
        Parameters defining the model.
    seed : np.random.RandomState, optional

    Returns
    -------
    elfi.ElfiModel

    """
    m = elfi.new_model()

    # Initialising the parameters as in Drovandi & Pettitt (2011).
    if true_params is None:
        true_params = [3, 4, 1, 0.5, 1, 2, .5, .4, 0.6]

    # Initialising the prior settings as in Drovandi & Pettitt (2011).
    priors = []
    priors.append(elfi.Prior('uniform', 0, 5, model=m, name='a1'))
    priors.append(elfi.Prior('uniform', 0, 5, model=m, name='a2'))
    priors.append(elfi.Prior('uniform', 0, 5, model=m, name='b1'))
    priors.append(elfi.Prior('uniform', 0, 5, model=m, name='b2'))
    priors.append(elfi.Prior('uniform', -5, 10, model=m, name='g1'))
    priors.append(elfi.Prior('uniform', -5, 10, model=m, name='g2'))
    priors.append(elfi.Prior('uniform', -.5, 5.5, model=m, name='k1'))
    priors.append(elfi.Prior('uniform', -.5, 5.5, model=m, name='k2'))
    EPS = np.finfo(float).eps

"""Initialise the g-and-k model.

    Parameters
    ----------
    n_obs : int, optional
        Number of the observations.
    true_params : array_like, optional
        Parameters defining the model.
    seed : np.random.RandomState, optional

    Returns
    -------
    elfi.ElfiModel

    """
    m = elfi.new_model()

    # Initialising the parameters as in Allingham et al. (2009).
    if true_params is None:
        true_params = [3, 1, 2, .5]

    # Initialising the prior settings as in Allingham et al. (2009).
    priors = []
    priors.append(elfi.Prior('uniform', 0, 10, model=m, name='A'))
    priors.append(elfi.Prior('uniform', 0, 10, model=m, name='B'))
    priors.append(elfi.Prior('uniform', 0, 10, model=m, name='g'))
    priors.append(elfi.Prior('uniform', 0, 10, model=m, name='k'))

    # Obtaining the observations.
    y_obs = GNK(*true_params, n_obs=n_obs, random_state=np.random.RandomState(seed))

    # Defining the simulator.