code: def slim_mul_mask(
      target: torch.Tensor, target_space: PruningTargetSpace
  ):
      scaling_factor = getattr(
          target_space._wrapper,
          f"{target_space._target_name}_slim_factor",
          None,
      )
      if scaling_factor is None:
          return mul_mask(target, target_space)
      else:
          assert target_space.shape is not None
          if target_space._scaler is not None:
              scaling_factor = target_space._scaler.expand(
                  scaling_factor,
                  target_space.shape,
                  keepdim=True,
                  full_expand=False,
              )
          return mul_mask(
              torch.mul(target, scaling_factor), target_space
          )
  
imports: from typing import Any
  import torch
  from .target_space import PruningTargetSpace, QuantizationTargetSpace, TargetSpace, TargetType
label_desc: Use image node_id 8 for calling a global function with example usage: slim_mul_mask(target, target_space) and returns: mul_mask, mul_mask