code: def prune_lstm_output_layernorm_linear(
      lstm: nn.LSTM,
      getitem: Callable,
      layernorm: Optional[nn.LayerNorm],
      linear: nn.Linear,
  ) -> None:
      for i in range(lstm.num_layers):
          if parametrize.is_parametrized(lstm, f"weight_ih_l{i}"):
              parametrization_dict = cast(
                  nn.ModuleDict, lstm.parametrizations
              )
              weight_parameterizations = cast(
                  ParametrizationList,
                  parametrization_dict[f"weight_ih_l{i}"],
              )
              mask = weight_parameterizations[0].mask
  
              with torch.no_grad():
                  parametrize.remove_parametrizations(
                      lstm, f"weight_ih_l{i}", leave_parametrized=True
                  )
                  setattr(
                      lstm,
                      f"weight_ih_l{i}",
                      nn.Parameter(
                          getattr(lstm, f"weight_ih_l{i}")[mask]
                      ),
                  )
                  setattr(
                      lstm,
                      f"bias_ih_l{i}",
                      nn.Parameter(
                          getattr(lstm, f"bias_ih_l{i}")[mask]
                      ),
                  )
  
          if parametrize.is_parametrized(lstm, f"weight_hh_l{i}"):
              parametrization_dict = cast(
                  nn.ModuleDict, lstm.parametrizations
              )
              weight_parameterizations = cast(
                  ParametrizationList,
                  parametrization_dict[f"weight_hh_l{i}"],
              )
              mask = weight_parameterizations[0].mask
  
              with torch.no_grad():
                  parametrize.remove_parametrizations(
                      lstm, f"weight_hh_l{i}", leave_parametrized=True
                  )
                  # splitting out hidden-hidden masks
                  W_hi, W_hf, W_hg, W_ho = torch.split(
                      getattr(lstm, f"weight_hh_l{i}"), lstm.hidden_size
                  )
                  M_hi, M_hf, M_hg, M_ho = torch.split(
                      mask, lstm.hidden_size
                  )
  
                  # resize each individual weight separately
                  W_hi = W_hi[M_hi][:, M_hi]
                  W_hf = W_hf[M_hf][:, M_hf]
                  W_hg = W_hg[M_hg][:, M_hg]
                  W_ho = W_ho[M_ho][:, M_ho]
  
                  # concat, use this as new weight
                  new_weight = torch.cat((W_hi, W_hf, W_hg, W_ho))
                  setattr(
                      lstm, f"weight_hh_l{i}", nn.Parameter(new_weight)
                  )
                  setattr(
                      lstm,
                      f"bias_hh_l{i}",
                      nn.Parameter(
                          getattr(lstm, f"bias_hh_l{i}")[mask]
                      ),
                  )
  
              # If this is the final layer, then we need to prune linear layer columns
              if i + 1 == lstm.num_layers:
                  lstm.hidden_size = int(M_hi.sum())
                  with torch.no_grad():
                      if parametrize.is_parametrized(linear):
                          parametrization_dict = cast(
                              nn.ModuleDict, linear.parametrizations
                          )
                          weight_parameterizations = cast(
                              ParametrizationList,
                              parametrization_dict.weight,
                          )
  
                          weight_parameterizations.original = (
                              nn.Parameter(
                                  weight_parameterizations.original[
                                      :, M_ho
                                  ]
                              )
                          )
                          linear.in_features = (
                              weight_parameterizations.original.shape[1]
                          )
                      else:
                          linear.weight = nn.Parameter(
                              linear.weight[:, M_ho]
                          )
                          linear.in_features = linear.weight.shape[1]
  
                      # if layernorm module, prune weight and bias
                      if layernorm is not None:
                          layernorm.normalized_shape = (
                              linear.in_features,
                          )
                          layernorm.weight = nn.Parameter(
                              layernorm.weight[M_ho]
                          )
                          layernorm.bias = nn.Parameter(
                              layernorm.bias[M_ho]
                          )
  
              # otherwise need to prune the columns of the input of the next LSTM layer
              else:
                  with torch.no_grad():
                      if parametrize.is_parametrized(
                          lstm, f"weight_ih_l{i+1}"
                      ):
                          parametrization_dict = cast(
                              nn.ModuleDict, lstm.parametrizations
                          )
                          weight_parameterizations = cast(
                              ParametrizationList,
                              getattr(
                                  parametrization_dict,
                                  f"weight_ih_l{i+1}",
                              ),
                          )
  
                          weight_parameterizations.original = (
                              nn.Parameter(
                                  weight_parameterizations.original[
                                      :, M_ho
                                  ]
                              )
                          )
                      else:
                          next_layer_weight = getattr(
                              lstm, f"weight_ih_l{i+1}"
                          )
                          setattr(
                              lstm,
                              f"weight_ih_l{i+1}",
                              nn.Parameter(next_layer_weight[:, M_ho]),
                          )
  
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 18 for calling a global function with example usage: prune_lstm_output_layernorm_linear(lstm, getitem, layernorm, linear) without return types