code: def forward(self, w: Tensor) -> Tensor:
      w_abs = torch.abs(w)
      if self.method == "surgery":
          mu, std = torch.mean(w_abs), torch.std(w_abs)
          zeros_mask = self.mask == 0.0
          ones_mask = self.mask == 1.0
          to_zeros_mask = ones_mask & (w_abs <= 0.9 * (mu - self.beta * std))  # type: ignore
          to_ones_mask = zeros_mask & (w_abs >= 1.1 * (mu + self.beta * std))  # type: ignore
          self.mask.masked_fill(to_zeros_mask, 0.0)  # type: ignore
          self.mask.masked_fill(to_ones_mask, 1.0)  # type: ignore
          mask = self.mask
          del (
              mu,
              std,
              ones_mask,
              zeros_mask,
              to_zeros_mask,
              to_ones_mask,
          )
      else:
          if self.method != "auto_prune":
              alpha, beta, ratio = self.alpha, self.beta, self.max_ratio
          else:
              alpha, beta, ratio = map(
                  F.softplus,
                  [self.alpha, self.beta, self.max_ratio],
              )
          if self.method == "simplified":
              log_w = torch.min(
                  ratio, beta * torch.pow(w_abs, self.exp)
              )
          else:
              w_abs_mean = torch.mean(w_abs)
              if self.method != "auto_prune":
                  gamma = self.gamma
              else:
                  gamma = F.softplus(self.gamma)
              log_w = torch.log(
                  torch.max(self.eps, w_abs / (w_abs_mean * gamma))
              )
              log_w = torch.min(ratio, beta * log_w)
              del w_abs_mean
          mask = torch.max(alpha / beta * log_w, log_w)
          del log_w
      w = w * mask
      del w_abs, mask
      return w
  
imports: import math
  import torch
  import numpy
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Tuple
  from typing import Union
  from typing import Callable
  from typing import Optional
  from functools import partial
  from torch.nn import Module
  from cftool.misc import print_warning
  from .hooks import IBasicHook
label_desc: Use image node_id 2 for calling the Pruner obj's underlying member method code with example usage: obj.forward(w) and returns: w