    def get_diagnostics(self,
                        iteration,
                        batch,
                        evaluation_paths,
                        training_paths):
        """Record diagnostic information.

        Records the mean and standard deviation of Q-function and the
        squared Bellman residual of the  s (mean squared Bellman error)
        for a sample batch.

        Also call the `draw` method of the plotter, if plotter is defined.
        """

        feeds = self._get_feed_dict(batch)
        Q_values, Q_losses =  self._session.run(
            [self._Q_values, self._Q_losses], feeds)

        diagnostics = OrderedDict({
            'Q-avg': np.mean(Q_values),
            'Q-std': np.std(Q_values),
            'Q_loss': np.mean(Q_losses),
        })

        policy_diagnostics = self._policy.get_diagnostics(batch['observations'])
        diagnostics.update({
            f'policy/{key}': value
            for key, value in policy_diagnostics.items()
        })

        if self._plotter:
            self._plotter.draw()

        return diagnostics
Record diagnostic information.

Records the mean and standard deviation of Q-function and the
squared Bellman residual of the  s (mean squared Bellman error)
for a sample batch.

Also call the `draw` method of the plotter, if plotter is defined.
softlearning/algorithms/sql.py