How to use the pykrige.variogram_models.exponential_variogram_model function in PyKrige

data, _, (ok_test_answer, gridx, gridy) = validation_ref

    ok = OrdinaryKriging(
        data[:, 0],
        data[:, 1],
        data[:, 2],
        variogram_model="exponential",
        variogram_parameters=_variogram_parameters,
    )

    # Get the variogram points from the UniversalKriging instance
    lags, calculated_variogram = ok.get_variogram_points()

    # Generate the expected variogram points according to the
    # exponential variogram model
    expected_variogram = variogram_models.exponential_variogram_model(
        _variogram_parameters, lags
    )

    assert_allclose(calculated_variogram, expected_variogram)

uk = UniversalKriging(
        data[:, 0],
        data[:, 1],
        data[:, 2],
        variogram_model="exponential",
        variogram_parameters=_variogram_parameters,
        drift_terms=["regional_linear"],
    )

    # Get the variogram points from the UniversalKriging instance
    lags, calculated_variogram = uk.get_variogram_points()

    # Generate the expected variogram points according to the
    # exponential variogram model
    expected_variogram = variogram_models.exponential_variogram_model(
        _variogram_parameters, lags
    )

    assert_allclose(calculated_variogram, expected_variogram)

assert_allclose(res, np.array([1.0, 1.5, 0.0]), 0.001, 0.001)

    res = core._calculate_variogram_model(
        np.array([1.0, 2.0, 3.0, 4.0]),
        np.array([1.0, 1.4142, 1.7321, 2.0]),
        "power",
        variogram_models.power_variogram_model,
        False,
    )
    assert_allclose(res, np.array([1.0, 0.5, 0.0]), 0.001, 0.001)

    res = core._calculate_variogram_model(
        np.array([1.0, 2.0, 3.0, 4.0]),
        np.array([1.2642, 1.7293, 1.9004, 1.9634]),
        "exponential",
        variogram_models.exponential_variogram_model,
        False,
    )
    assert_allclose(res, np.array([2.0, 3.0, 0.0]), 0.001, 0.001)

    res = core._calculate_variogram_model(
        np.array([1.0, 2.0, 3.0, 4.0]),
        np.array([0.5769, 1.4872, 1.9065, 1.9914]),
        "gaussian",
        variogram_models.gaussian_variogram_model,
        False,
    )
    assert_allclose(res, np.array([2.0, 3.0, 0.0]), 0.001, 0.001)

    res = core._calculate_variogram_model(
        np.array([1.0, 2.0, 3.0, 4.0]),
        np.array([3.33060952, 3.85063879, 3.96667301, 3.99256374]),

assert_allclose(res, np.array([2.0, 3.0, 0.0]), 0.001, 0.001)

    res = core._calculate_variogram_model(
        np.array([1.0, 2.0, 3.0, 4.0]),
        np.array([0.5769, 1.4872, 1.9065, 1.9914]),
        "gaussian",
        variogram_models.gaussian_variogram_model,
        False,
    )
    assert_allclose(res, np.array([2.0, 3.0, 0.0]), 0.001, 0.001)

    res = core._calculate_variogram_model(
        np.array([1.0, 2.0, 3.0, 4.0]),
        np.array([3.33060952, 3.85063879, 3.96667301, 3.99256374]),
        "exponential",
        variogram_models.exponential_variogram_model,
        False,
    )
    assert_allclose(res, np.array([3.0, 2.0, 1.0]), 0.001, 0.001)

    res = core._calculate_variogram_model(
        np.array([1.0, 2.0, 3.0, 4.0]),
        np.array([2.60487044, 3.85968813, 3.99694817, 3.99998564]),
        "gaussian",
        variogram_models.gaussian_variogram_model,
        False,
    )
    assert_allclose(res, np.array([3.0, 2.0, 1.0]), 0.001, 0.001)

enable_plotting : bool, optional
        Enables plotting to display variogram. Default is False (off).

    References
    ----------
    .. [1] P.K. Kitanidis, Introduction to Geostatistcs: Applications in
       Hydrogeology, (Cambridge University Press, 1997) 272 p.
    """

    eps = 1.0e-10  # Cutoff for comparison to zero
    variogram_dict = {
        "linear": variogram_models.linear_variogram_model,
        "power": variogram_models.power_variogram_model,
        "gaussian": variogram_models.gaussian_variogram_model,
        "spherical": variogram_models.spherical_variogram_model,
        "exponential": variogram_models.exponential_variogram_model,
        "hole-effect": variogram_models.hole_effect_variogram_model,
    }

    def __init__(
        self,
        x,
        y,
        z,
        val,
        variogram_model="linear",
        variogram_parameters=None,
        variogram_function=None,
        nlags=6,
        weight=False,
        anisotropy_scaling_y=1.0,
        anisotropy_scaling_z=1.0,

References
    ----------
    .. [1] P.K. Kitanidis, Introduction to Geostatistcs: Applications in
       Hydrogeology, (Cambridge University Press, 1997) 272 p.
    """

    UNBIAS = True  # This can be changed to remove the unbiasedness condition
    # Really for testing purposes only...
    eps = 1.0e-10  # Cutoff for comparison to zero
    variogram_dict = {
        "linear": variogram_models.linear_variogram_model,
        "power": variogram_models.power_variogram_model,
        "gaussian": variogram_models.gaussian_variogram_model,
        "spherical": variogram_models.spherical_variogram_model,
        "exponential": variogram_models.exponential_variogram_model,
        "hole-effect": variogram_models.hole_effect_variogram_model,
    }

    def __init__(
        self,
        x,
        y,
        z,
        variogram_model="linear",
        variogram_parameters=None,
        variogram_function=None,
        nlags=6,
        weight=False,
        anisotropy_scaling=1.0,
        anisotropy_angle=0.0,
        drift_terms=None,

References
    ----------
    .. [1] P.K. Kitanidis, Introduction to Geostatistcs: Applications in
       Hydrogeology, (Cambridge University Press, 1997) 272 p.
    """

    UNBIAS = True  # This can be changed to remove the unbiasedness condition
    # Really for testing purposes only...
    eps = 1.0e-10  # Cutoff for comparison to zero
    variogram_dict = {
        "linear": variogram_models.linear_variogram_model,
        "power": variogram_models.power_variogram_model,
        "gaussian": variogram_models.gaussian_variogram_model,
        "spherical": variogram_models.spherical_variogram_model,
        "exponential": variogram_models.exponential_variogram_model,
        "hole-effect": variogram_models.hole_effect_variogram_model,
    }

    def __init__(
        self,
        x,
        y,
        z,
        val,
        variogram_model="linear",
        variogram_parameters=None,
        variogram_function=None,
        nlags=6,
        weight=False,
        anisotropy_scaling_y=1.0,
        anisotropy_scaling_z=1.0,

coordinates, both given in degree. Longitudes are expected in
        [0, 360] and latitudes in [-90, 90]. Default is 'euclidean'.

    References
    ----------
    .. [1] P.K. Kitanidis, Introduction to Geostatistcs: Applications in
       Hydrogeology, (Cambridge University Press, 1997) 272 p.
    """

    eps = 1.0e-10  # Cutoff for comparison to zero
    variogram_dict = {
        "linear": variogram_models.linear_variogram_model,
        "power": variogram_models.power_variogram_model,
        "gaussian": variogram_models.gaussian_variogram_model,
        "spherical": variogram_models.spherical_variogram_model,
        "exponential": variogram_models.exponential_variogram_model,
        "hole-effect": variogram_models.hole_effect_variogram_model,
    }

    def __init__(
        self,
        x,
        y,
        z,
        variogram_model="linear",
        variogram_parameters=None,
        variogram_function=None,
        nlags=6,
        weight=False,
        anisotropy_scaling=1.0,
        anisotropy_angle=0.0,
        verbose=False,