code: def __call__(self, module, input, output):
      if getattr(module, "_bias", None) is not None:
          bias = module._bias.data
          if self.prune_bias:
              bias[~self.param.mask] = 0
  
          # reshape bias to broadcast over output dimensions
          idx = [1] * len(output.shape)
          idx[1] = -1
          bias = bias.reshape(idx)
  
          output += bias
      return output
  
file_path: pytorch/torch/ao/pruning/_experimental/pruner/parametrization.py
imports: import torch
  from torch import nn
  from torch.nn.utils.parametrize import is_parametrized
label_desc: Use image node_id 2 for calling the BiasHook obj's underlying member method code with example usage: obj.__call__(module, input, output) and returns: output