code: def backward(ctx: Any, grad_output: Any) -> Any:
      (target,) = ctx.saved_variables
      target_space = ctx.target_space
      if target_space.type == TargetType.OUTPUT:
          grad_output[torch.abs(target) > 1] = 0
  
      return grad_output, None
  
imports: from typing import Any
  import torch
  from .target_space import PruningTargetSpace, QuantizationTargetSpace, TargetSpace, TargetType
label_desc: Use image node_id 2 for calling the BNNClampRound obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: grad_output,  None