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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 _build_prior(self, unconstrained_tensor, constrained_tensor):
 """ Build a tensorflow representation of the prior density. The log Jacobian is included. """

        if not misc.is_tensor(unconstrained_tensor):
            raise GPflowError("Unconstrained input must be a tensor.")

        if not misc.is_tensor(constrained_tensor):
            raise GPflowError("Constrained input must be a tensor.")

        prior_name = 'prior'

        if self.prior is None:
            return tf.constant(0.0, settings.float_type, name=prior_name)

        log_jacobian = self.transform.log_jacobian_tensor(unconstrained_tensor)
        logp_var = self.prior.logp(constrained_tensor)
        return tf.squeeze(tf.add(logp_var, log_jacobian, name=prior_name))