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`
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docstr: Given a list of indices, prune the weights at those indices.
  
  Pruning simply marks the weight as pruned in the `task_index` and
  makes the weight invisible in the `visible_mask`.
  
  :param indices: A 1D tensor of indices to prune
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code: def _prune_weights(self, indices: Tensor):
      """Given a list of indices, prune the weights at those indices.
  
      Pruning simply marks the weight as pruned in the `task_index` and
      makes the weight invisible in the `visible_mask`.
  
      :param indices: A 1D tensor of indices to prune
      """
      self.task_index.flatten()[indices] = self.PRUNED_CODE.item()
      self.visible_mask.flatten()[indices] = False
  
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 10 for calling the WeightAndBiasPackNetModule obj's underlying member method code with example usage: obj._prune_weights(indices) without return types