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

np.random.choice(
                    range(self.samples_per_posterior),
                    size=self.samples_per_posterior,
                    replace=True,
                    p=to_numpy(weights[ii]),
                )
            )
        new_samples = {
            key: xp.vstack(
                [self.data[key][ii, new_idxs[ii]] for ii in range(self.n_posteriors)]
            )
            for key in self.data
        }
        event_weights = list(event_weights)
        weight_string = " ".join([f"{float(weight):.1f}" for weight in event_weights])
        logger.info(f"Resampling done, sum of weights for events are {weight_string}")
        if return_weights:
            return new_samples, weights
        else:
            return new_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:
            self.total_noise_evidence = np.sum(ln_evidences)
        else:
            self.total_noise_evidence = np.nan

        self.conversion_function = conversion_function
        self.selection_function = selection_function

        self.n_posteriors = len(posteriors)