code: def forward(self, net: Tensor) -> Tensor:
      if self.randomize_at_zero:
          net = (
              net
              + (2 * torch.empty_like(net).uniform_() - 1.0) * self.eps
          )
      sign = torch.sign(net)
      if not self.differentiable:
          return sign
      return net + (sign - net).detach()
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from abc import ABCMeta
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import Type
  from typing import Optional
  from functools import partial
  from torch.nn import Module
  from cftool.misc import WithRegister
  from .common import Lambda
  from .hijacks import HijackLinear
label_desc: Use image node_id 2 for calling the Sign obj's underlying member method code with example usage: obj.forward(net) and returns: unknown, sign