code: def forward(
      ctx: Any,
      target: torch.Tensor,
      target_space: QuantizationTargetSpace,
  ) -> Any:
      ctx.save_for_backward(target)
      signed_target = torch.sign(target)
      signed_target[signed_target == 0] = 1
      ctx.target_space = target_space
      return signed_target
  
imports: from typing import Any
  import torch
  from .target_space import PruningTargetSpace, QuantizationTargetSpace, TargetSpace, TargetType
label_desc: Use image node_id 1 for calling the BNNClampRound obj's underlying member method code with example usage: obj.forward(ctx, target, target_space) and returns: signed_target