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 device(self) -> torch.device:
      return self.weight.device
  
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 7 for calling the WeightAndBiasPackNetModule obj's underlying member method code with example usage: obj.device() and returns: self