code: def dorefa_clamp_round_weight(
      target: torch.Tensor, target_space: QuantizationTargetSpace
  ) -> Any:
      # TODO process special case: quant_bit == 1
      target = target.tanh()
      target = target / (2 * target.abs().max()) + 0.5
      dequantized_target = ClampRound.apply(target, target_space)
  
      return 2 * dequantized_target - 1
  
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 DoferaGradClampRound obj's underlying member method code with example usage: obj.dorefa_clamp_round_weight(target, target_space) and returns: unknown