How to use the bilby.hyper.model.Model function in bilby

Function which converts a dictionary of sampled parameter to a
            dictionary of parameters of the population model.
        max_samples: int, optional
            Maximum number of samples to use from each set.
        cupy: bool
            If True and a compatible CUDA environment is available,
            cupy will be used for performance.
            Note: this requires setting up your hyper_prior properly.
        """
        if cupy and not CUPY_LOADED:
            logger.warning("Cannot import cupy, falling back to numpy.")

        self.samples_per_posterior = max_samples
        self.data = self.resample_posteriors(posteriors, max_samples=max_samples)

        if not isinstance(hyper_prior, Model):
            hyper_prior = Model([hyper_prior])
        self.hyper_prior = hyper_prior
        Likelihood.__init__(self, hyper_prior.parameters)

        if sampling_prior is not None:
            raise ValueError(
                "Passing a sampling_prior is deprecated and will be removed "
                "in the next release. This should be passed as a 'prior' "
                "column in the posteriors."
            )
        elif "prior" in self.data:
            self.sampling_prior = self.data.pop("prior")
        else:
            logger.info("No prior values provided, defaulting to 1.")
            self.sampling_prior = 1

def __init__(self, model, data):
        self.vts = data.pop("vt")
        self.data = data
        if isinstance(model, list):
            model = Model(model)
        elif not isinstance(model, Model):
            model = Model([model])
        self.model = model
        self.values = {key: xp.unique(self.data[key]) for key in self.data}
        shape = np.array(list(self.data.values())[0].shape)
        lens = {key: len(self.values[key]) for key in self.data}
        self.axes = {int(np.where(shape == lens[key])[0]): key for key in self.data}
        self.ndim = len(self.axes)

dictionary of parameters of the population model.
        max_samples: int, optional
            Maximum number of samples to use from each set.
        cupy: bool
            If True and a compatible CUDA environment is available,
            cupy will be used for performance.
            Note: this requires setting up your hyper_prior properly.
        """
        if cupy and not CUPY_LOADED:
            logger.warning("Cannot import cupy, falling back to numpy.")

        self.samples_per_posterior = max_samples
        self.data = self.resample_posteriors(posteriors, max_samples=max_samples)

        if not isinstance(hyper_prior, Model):
            hyper_prior = Model([hyper_prior])
        self.hyper_prior = hyper_prior
        Likelihood.__init__(self, hyper_prior.parameters)

        if sampling_prior is not None:
            raise ValueError(
                "Passing a sampling_prior is deprecated and will be removed "
                "in the next release. This should be passed as a 'prior' "
                "column in the posteriors."
            )
        elif "prior" in self.data:
            self.sampling_prior = self.data.pop("prior")
        else:
            logger.info("No prior values provided, defaulting to 1.")
            self.sampling_prior = 1

        if ln_evidences is not None:

def __init__(self, model, data):
        self.vts = data.pop("vt")
        self.data = data
        if isinstance(model, list):
            model = Model(model)
        elif not isinstance(model, Model):
            model = Model([model])
        self.model = model
        self.values = {key: xp.unique(self.data[key]) for key in self.data}
        shape = np.array(list(self.data.values())[0].shape)
        lens = {key: len(self.values[key]) for key in self.data}
        self.axes = {int(np.where(shape == lens[key])[0]): key for key in self.data}
        self.ndim = len(self.axes)

def __init__(self, model, data):
        self.vts = data.pop("vt")
        self.data = data
        if isinstance(model, list):
            model = Model(model)
        elif not isinstance(model, Model):
            model = Model([model])
        self.model = model
        self.values = {key: xp.unique(self.data[key]) for key in self.data}
        shape = np.array(list(self.data.values())[0].shape)
        lens = {key: len(self.values[key]) for key in self.data}
        self.axes = {int(np.where(shape == lens[key])[0]): key for key in self.data}
        self.ndim = len(self.axes)