    def get_diagnostics(self, conditions):
        """Return diagnostic information of the policy.

        Returns the mean, min, max, and standard deviation of means and
        covariances.
        """
        (shifts_np,
         log_scale_diags_np,
         log_pis_np,
         raw_actions_np,
         actions_np) = self.diagnostics_model.predict(conditions)

        return OrderedDict({
            'shifts-mean': np.mean(shifts_np),
            'shifts-std': np.std(shifts_np),

            'log_scale_diags-mean': np.mean(log_scale_diags_np),
            'log_scale_diags-std': np.std(log_scale_diags_np),

            '-log-pis-mean': np.mean(-log_pis_np),
            '-log-pis-std': np.std(-log_pis_np),

            'raw-actions-mean': np.mean(raw_actions_np),
            'raw-actions-std': np.std(raw_actions_np),

            'actions-mean': np.mean(actions_np),
            'actions-std': np.std(actions_np),
        })
Return diagnostic information of the policy.

Returns the mean, min, max, and standard deviation of means and
covariances.
softlearning/policies/gaussian_policy.py