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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def model_stats(self):
 """Returns the log likelihood ratio and log prior as a dict of arrays. The returned array has shape ntemps x nwalkers x niterations. Unfortunately, because ``emcee_pt`` does not have blob support, this will only return the loglikelihood and logprior (with the logjacobian set to zero) regardless of what stats the model can return. .. warning:: Since the `logjacobian` is not saved by `emcee_pt`, the `logprior` returned here is the log of the prior pdf in the sampling coordinate frame rather than the variable params frame. This differs from the variable params frame by the log of the Jacobian of the transform from one frame to the other. If no sampling transforms were used, then the `logprior` is the same. """

        # likelihood has shape ntemps x nwalkers x niterations
        logl = self._sampler.lnlikelihood
        # get prior from posterior
        logp = self._sampler.lnprobability - logl
        logjacobian = numpy.zeros(logp.shape)
        return {'loglikelihood': logl, 'logprior': logp,
                'logjacobian': logjacobian}