def before_training_epoch(self, strategy: "BaseSGDTemplate", *args, **kwargs):
        """When the initial training phase is over, prune the model and
        transition to the post-pruning phase.
        """
        epoch = strategy.clock.train_exp_epochs
        model = self._get_model(strategy)

        if epoch == (self.total_epochs - self.post_prune_epochs):
            model.prune(self.prune_proportion(strategy.clock.train_exp_counter))
When the initial training phase is over, prune the model and
        transition to the post-pruning phase.
        
avalanche/models/packnet.py