class_docstr: A PackNet module that has a weight and bias. This can be used to wrap
  many PyTorch modules such as `nn.Linear`, `nn.Conv2d` and `nn.ConvTranspose2d`
class_docstr_tok: ["A","PackNet","module","that","has","a","weight","and","bias",".","This","can","be","used","to","wrap","many","PyTorch","modules","such","as","`","nn.Linear","`",",","`","nn.Conv2d","`","and","`","nn.ConvTranspose2d","`"]
code: def _activate_task(self, task_id: int):
      self.visible_mask.zero_()
      self.unfrozen_mask.zero_()
      self.visible_mask = self.task_index.less_equal(task_id)
      if task_id == self.task_count:
          self.unfrozen_mask = self.task_index.eq(task_id)
  
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 WeightAndBiasPackNetModule obj's underlying member method code with example usage: obj._activate_task(task_id) without return types