code: def prune_conv2d_padded(conv2d_1: nn.Conv2d) -> None:
      parametrization_dict = cast(
          nn.ModuleDict, conv2d_1.parametrizations
      )
      weight_parameterizations = cast(
          ParametrizationList, parametrization_dict.weight
      )
      for p in weight_parameterizations:
          if isinstance(p, FakeStructuredSparsity):
              mask = cast(Tensor, p.mask)
  
      with torch.no_grad():
          parametrize.remove_parametrizations(
              conv2d_1, "weight", leave_parametrized=True
          )
  
      if getattr(conv2d_1, "_bias", None) is not None:
          if (
              conv2d_1.bias is not None
          ):  # conv2d_1 has original bias and bias propagated from previous layer
              new_bias = torch.zeros(conv2d_1.bias.shape)
              new_bias[mask] = conv2d_1.bias[mask]
              # adjusted bias that to keep in conv2d_1
              new_bias[~mask] = cast(Tensor, conv2d_1._bias)[~mask]
              # pruned biases that are kept instead of propagated
              conv2d_1.bias = nn.Parameter(new_bias)
          else:  # conv2d_1 has only original bias
              conv2d_1.bias = nn.Parameter(cast(Tensor, conv2d_1._bias))
      else:
          # no original bias, only propagated bias
          if (
              conv2d_1.bias is not None
          ):  # conv2d_1 has bias propagated from previous layer
              conv2d_1.bias.data[~mask] = 0
  
      if hasattr(conv2d_1, "_bias"):
          delattr(conv2d_1, "_bias")
  
imports: from typing import cast, List, Optional, Callable, Tuple
  import torch
  from torch import nn, Tensor
  from torch.nn.utils import parametrize
  from torch.nn.utils.parametrize import ParametrizationList
  from .parametrization import FakeStructuredSparsity, BiasHook
label_desc: Use image node_id 10 for calling a global function with example usage: prune_conv2d_padded(conv2d_1) without return types