How to use the bilby.core.utils.logger.warning function in bilby

evidences will be set to 0. This produces a Bayes factor between
            the sampling power_prior and the hyperparameterised model.
        selection_function: func
            Function which evaluates your population selection function.
        conversion_function: func
            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: