How to use the pyhf.simplemodels.hepdata_like function in pyhf

pdf = pyhf.simplemodels.hepdata_like(
        source['bindata']['sig'], source['bindata']['bkg'], source['bindata']['bkgerr']
    )

    pars = pdf.config.suggested_init()
    data = source['bindata']['data'] + pdf.config.auxdata

    tensorlib, _ = backend
    assert tensorlib.tolist(pdf.pdf(pars, data)) == pytest.approx(
        [0.002417118312751542], 2.5e-05
    )
    assert tensorlib.tolist(pdf.expected_data(pars)) == pytest.approx(
        [60.0, 51.020408630], 1e-08
    )

    pdf = pyhf.simplemodels.hepdata_like(
        source['bindata']['sig'],
        source['bindata']['bkg'],
        source['bindata']['bkgerr'],
        batch_size=2,
    )

    pars = [pdf.config.suggested_init()] * 2
    data = source['bindata']['data'] + pdf.config.auxdata

    tensorlib, _ = backend
    assert tensorlib.tolist(pdf.pdf(pars, data)) == pytest.approx(
        [0.002417118312751542] * 2, 2.5e-05
    )
    assert tensorlib.tolist(pdf.expected_data(pars))
    assert tensorlib.tolist(pdf.expected_data(pars)[0]) == pytest.approx(
        [60.0, 51.020408630], 1e-08

def model_setup(backend):
    np.random.seed(0)
    n_bins = 100
    model = pyhf.simplemodels.hepdata_like([10] * n_bins, [50] * n_bins, [1] * n_bins)
    init_pars = model.config.suggested_init()
    observations = np.random.randint(50, 60, size=n_bins).tolist()
    data = observations + model.config.auxdata
    return model, data, init_pars

def test_pdf_inputs(backend):
    source = {
        "binning": [2, -0.5, 1.5],
        "bindata": {"data": [55.0], "bkg": [50.0], "bkgerr": [7.0], "sig": [10.0]},
    }
    pdf = pyhf.simplemodels.hepdata_like(
        source['bindata']['sig'], source['bindata']['bkg'], source['bindata']['bkgerr']
    )

    pars = pdf.config.suggested_init()
    data = source['bindata']['data'] + pdf.config.auxdata

    tensorlib, _ = backend
    assert tensorlib.shape(tensorlib.astensor(data)) == (2,)
    assert tensorlib.shape(tensorlib.astensor(pars)) == (2,)
    assert tensorlib.tolist(pdf.pdf(pars, data)) == pytest.approx(
        [0.002417160663753748], abs=1e-4
    )
    assert tensorlib.tolist(pdf.logpdf(pars, data)) == pytest.approx(
        [-6.025179228209936], abs=1e-4
    )

def test_pdf_basicapi_tests(backend):
    source = {
        "binning": [2, -0.5, 1.5],
        "bindata": {"data": [55.0], "bkg": [50.0], "bkgerr": [7.0], "sig": [10.0]},
    }
    pdf = pyhf.simplemodels.hepdata_like(
        source['bindata']['sig'], source['bindata']['bkg'], source['bindata']['bkgerr']
    )

    pars = pdf.config.suggested_init()
    data = source['bindata']['data'] + pdf.config.auxdata

    tensorlib, _ = backend
    assert tensorlib.tolist(pdf.pdf(pars, data)) == pytest.approx(
        [0.002417118312751542], 2.5e-05
    )
    assert tensorlib.tolist(pdf.expected_data(pars)) == pytest.approx(
        [60.0, 51.020408630], 1e-08
    )

    pdf = pyhf.simplemodels.hepdata_like(
        source['bindata']['sig'],

def test_smoketest_toys(backend):
    tb, _ = pyhf.get_backend()
    m = pyhf.simplemodels.hepdata_like([6], [9], [3])
    s = m.make_pdf(pyhf.tensorlib.astensor(m.config.suggested_init()))
    assert np.asarray(tb.tolist(s.log_prob(s.sample((1000,))))).shape == (1000,)

    tb, _ = pyhf.get_backend()
    m = pyhf.simplemodels.hepdata_like([6, 6], [9, 9], [3, 3], batch_size=13)
    s = m.make_pdf(pyhf.tensorlib.astensor(m.batch_size * [m.config.suggested_init()]))
    assert np.asarray(tb.tolist(s.sample((10,)))).shape == (10, 13, 4)

def test_smoketest_toys(backend):
    tb, _ = pyhf.get_backend()
    m = pyhf.simplemodels.hepdata_like([6], [9], [3])
    s = m.make_pdf(pyhf.tensorlib.astensor(m.config.suggested_init()))
    assert np.asarray(tb.tolist(s.log_prob(s.sample((1000,))))).shape == (1000,)

    tb, _ = pyhf.get_backend()
    m = pyhf.simplemodels.hepdata_like([6, 6], [9, 9], [3, 3], batch_size=13)
    s = m.make_pdf(pyhf.tensorlib.astensor(m.batch_size * [m.config.suggested_init()]))
    assert np.asarray(tb.tolist(s.sample((10,)))).shape == (10, 13, 4)

def test_hypotest(benchmark, backend, n_bins):
    """
    Benchmark the performance of pyhf.utils.hypotest()
    for various numbers of bins and different backends

    Args:
        benchmark: pytest benchmark
        backend: `pyhf` tensorlib given by pytest parameterization
        n_bins: `list` of number of bins given by pytest parameterization

    Returns:
        None
    """
    source = generate_source_static(n_bins)
    pdf = hepdata_like(
        source['bindata']['sig'], source['bindata']['bkg'], source['bindata']['bkgerr']
    )
    data = source['bindata']['data'] + pdf.config.auxdata
    assert benchmark(hypotest, pdf, data)

def test_pdf_batched(backend):
    tb, _ = backend
    source = {
        "binning": [2, -0.5, 1.5],
        "bindata": {"data": [55.0], "bkg": [50.0], "bkgerr": [7.0], "sig": [10.0]},
    }
    model = pyhf.simplemodels.hepdata_like(
        source['bindata']['sig'],
        source['bindata']['bkg'],
        source['bindata']['bkgerr'],
        batch_size=2,
    )

    pars = [model.config.suggested_init()] * 2
    data = source['bindata']['data'] + model.config.auxdata

    model.pdf(pars, data)
    model.expected_data(pars)

def test_invalid_pdf_pars():
    source = {
        "binning": [2, -0.5, 1.5],
        "bindata": {"data": [55.0], "bkg": [50.0], "bkgerr": [7.0], "sig": [10.0]},
    }
    pdf = pyhf.simplemodels.hepdata_like(
        source['bindata']['sig'], source['bindata']['bkg'], source['bindata']['bkgerr']
    )

    pars = pdf.config.suggested_init() + [1.0]
    data = source['bindata']['data'] + pdf.config.auxdata

    with pytest.raises(pyhf.exceptions.InvalidPdfParameters):
        pdf.logpdf(pars, data)