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","."]
code: def before_training(
      self, strategy: "BaseSGDTemplate", *args, **kwargs
  ):
      assert isinstance(
          strategy, BaseSGDTemplate
      ), "Strategy must be a `BaseSGDTemplate` or derived class."
  
      if not hasattr(strategy, "train_epochs"):
          raise ValueError(
              "`PackNetPlugin` can only be used with a `BaseStrategy` that "
              "has a `train_epochs` attribute."
          )
  
      # Check the scenario has enough epochs for the post-pruning phase
      self.total_epochs = strategy.train_epochs
      if self.post_prune_epochs >= self.total_epochs:
          raise ValueError(
              f"`PackNetPlugin` can only be used with a `BaseStrategy`"
              "that has a `train_epochs` attribute greater than "
              f"{self.post_prune_epochs}. "
              f"Strategy has only {self.total_epochs} training epochs."
          )
  
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 2 for calling the PackNetPlugin obj's underlying member method code with example usage: obj.before_training(strategy) without return types