code: def _collect_data(self) -> Dict[str, Dict[str, torch.Tensor]]:
      data = defaultdict(dict)
      for module_name, ts in self._target_spaces.items():
          for target_name, target_space in ts.items():
              scaling_factor: torch.Tensor = getattr(
                  target_space._wrapper,
                  SLIM_SCALING_FACTOR_PNAME.format(target_name),
                  None,
              )  # type: ignore
              if scaling_factor is not None:
                  data[module_name][
                      target_name
                  ] = scaling_factor.clone().detach()
      return data
  
imports: from __future__ import annotations
  from collections import defaultdict
  import logging
  from typing import Dict, List, Literal, Tuple, overload
  import torch
  from torch.optim import Adam
  from .tools import _METRICS, _MASKS, generate_sparsity, is_active_target
  from ..base.compressor import Compressor, Pruner
  from ..base.target_space import TargetType
  from ..base.wrapper import ModuleWrapper
  from ..utils import Evaluator, _EVALUATOR_DOCSTRING
label_desc: Use image node_id 9 for calling the SlimPruner obj's underlying member method code with example usage: obj._collect_data() and returns: data