class_docstr: A plugin calling PackNet's pruning and freezing procedures at the
  appropriate times. This plugin can only be used with `PackNet` models.
class_docstr_tok: ["A","plugin","calling","PackNet","'s","pruning","and","freezing","procedures","at","the","appropriate","times",".","This","plugin","can","only","be","used","with","`","PackNet","`","models","."]
docstr: After each experience, commit the model so that the next experience
  does not interfere with the previous one.
docstr_tok: ["After","each","experience",",","commit","the","model","so","that","the","next","experience","does","not","interfere","with","the","previous","one","."]
code: def after_training_exp(self, strategy: "Template", *args, **kwargs):
      """After each experience, commit the model so that the next experience
      does not interfere with the previous one.
      """
      model = self._get_model(strategy)
      model.freeze_pruned()
  
imports: import typing
  import torch
  import torch.nn
  from torch import Tensor
  from torch.nn import functional
  from abc import ABC, abstractmethod
  from enum import Enum
  from avalanche.core import BaseSGDPlugin, Template
  from avalanche.models.dynamic_modules import MultiTaskModule
  from avalanche.models.simple_mlp import SimpleMLP
  from typing import Union
  from avalanche.training.templates.base_sgd import BaseSGDTemplate
label_desc: Use image node_id 5 for calling the PackNetPlugin obj's underlying member method code with example usage: obj.after_training_exp(strategy) without return types