code: def forward(
      ctx: Any,
      target: torch.Tensor,
      target_space: QuantizationTargetSpace,
  ) -> Any:
      if target_space._scaler is not None:
          zero_point = target_space._scaler.expand(target_space.zero_point, target_space.shape, keepdim=True)  # type: ignore
          scale = target_space._scaler.expand(target_space.scale, target_space.shape, keepdim=True)  # type: ignore
      else:
          zero_point = target_space.zero_point
          scale = target_space.scale
      transformed_target = zero_point + target / scale
      quantized_target = torch.round(
          torch.clamp(
              transformed_target, target_space.qmin, target_space.qmax
          )
      )
      dequantized_target = (quantized_target - zero_point) * scale
      return dequantized_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 ClampRound obj's underlying member method code with example usage: obj.forward(ctx, target, target_space) and returns: dequantized_target