How to use the exoplanet.transforms function in exoplanet

def __init__(self, lower=0, upper=1, **kwargs):
        self.lower = lower
        self.upper = upper

        transform = kwargs.pop("transform", None)
        if transform is None:
            transform = tr.PeriodicTransform(
                lower=lower,
                upper=upper,
                regularized=kwargs.pop("regularized", 10.0),
            )
        kwargs["transform"] = transform

        shape = kwargs.get("shape", None)
        if shape is None:
            testval = 0.5 * (lower + upper)
        else:
            testval = 0.5 * (lower + upper) + np.zeros(shape)
        kwargs["testval"] = kwargs.pop("testval", testval)
        super(Periodic, self).__init__(**kwargs)

def __init__(self, *args, **kwargs):
        add_citations_to_model(self.__citations__, kwargs.get("model", None))

        # Make sure that the shape is compatible
        shape = kwargs.get("shape", 2)
        try:
            if list(shape)[0] != 2:
                raise ValueError("the first dimension should be exactly 2")
        except TypeError:
            if shape != 2:
                raise ValueError("the first dimension should be exactly 2")

        self.min_radius = kwargs.pop("min_radius", 0)
        self.max_radius = kwargs.pop("max_radius", 1)
        transform = tr.radius_impact(self.min_radius, self.max_radius)
        kwargs["shape"] = shape
        kwargs["transform"] = transform

        super(RadiusImpact, self).__init__(*args, **kwargs)

        # Work out some reasonable starting values for the parameters
        default = np.zeros(shape)
        mn, mx = draw_values([self.min_radius - 0.0, self.max_radius - 0.0])
        default[0] = 0.5 * (mn + mx)
        default[1] = 0.5
        self._default = default

def __init__(self, *args, **kwargs):
        kwargs["transform"] = tr.unit_vector
        super(UnitVector, self).__init__(*args, **kwargs)

def __init__(self, ror=None, **kwargs):
        if ror is None:
            raise ValueError("missing required parameter 'ror'")
        self.ror = tt.as_tensor_variable(ror)
        kwargs["transform"] = kwargs.pop(
            "transform", tr.ImpactParameterTransform(self.ror)
        )

        shape = kwargs.get("shape", None)
        if shape is None:
            testval = 0.5
        else:
            testval = 0.5 + np.zeros(shape)
        kwargs["testval"] = kwargs.pop("testval", testval)

        super(ImpactParameter, self).__init__(**kwargs)

def __init__(self, *args, **kwargs):
        transform = kwargs.pop("transform", None)
        if transform is None:
            if "regularized" in kwargs:
                transform = tr.AngleTransform(
                    regularized=kwargs.pop("regularized")
                )
            else:
                transform = tr.angle
        kwargs["transform"] = transform

        shape = kwargs.get("shape", None)
        if shape is None:
            testval = 0.0
        else:
            testval = np.zeros(shape)
        kwargs["testval"] = kwargs.pop("testval", testval)
        super(Angle, self).__init__(*args, **kwargs)

def __init__(self, *args, **kwargs):
        add_citations_to_model(self.__citations__, kwargs.get("model", None))

        # Make sure that the shape is compatible
        shape = kwargs.get("shape", 2)
        try:
            if list(shape)[0] != 2:
                raise ValueError("the first dimension should be exactly 2")
        except TypeError:
            if shape != 2:
                raise ValueError("the first dimension should be exactly 2")

        kwargs["shape"] = shape
        kwargs["transform"] = tr.quad_limb_dark

        super(QuadLimbDark, self).__init__(*args, **kwargs)

        # Work out some reasonable starting values for the parameters
        default = np.zeros(shape)
        default[0] = np.sqrt(0.5)
        default[1] = 0.0
        self._default = default

def __init__(self, *args, **kwargs):
        transform = kwargs.pop("transform", None)
        if transform is None:
            if "regularized" in kwargs:
                transform = tr.AngleTransform(
                    regularized=kwargs.pop("regularized")
                )
            else:
                transform = tr.angle
        kwargs["transform"] = transform

        shape = kwargs.get("shape", None)
        if shape is None:
            testval = 0.0
        else:
            testval = np.zeros(shape)
        kwargs["testval"] = kwargs.pop("testval", testval)
        super(Angle, self).__init__(*args, **kwargs)